This document describes how to go from raw vcftools output of diversity metrics (Fst, pi, and Tajima’s D) to Manhattan plots, including making figures for the manuscript associated with this repo. This script is a result of brute forcing things to work: I am certainly no expert, and thus lots of the notes refer to my own dumb mistakes.

library(tidyverse)
library(qqman)
library(scales)

Prepare files: join vcftools output into a single table.

First, read in raw output files from vcftools (in this repo, see filter-scan.sh for code to generate).

fst.UKUS.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseud_nostep_UKUS_50kb.windowed.weir.fst",sep="\t"))
fst.AUUK.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseud_nostep_AUUK_50kb.windowed.weir.fst",sep="\t"))
fst.USAU.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseud_nostep_AUUS_50kb.windowed.weir.fst",sep="\t"))
pi.UK.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_UK_pi_50kb.windowed.pi",sep="\t"))
pi.AU.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_AU_pi_50kb.windowed.pi",sep="\t"))
pi.US.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_US_pi_50kb.windowed.pi",sep="\t"))
TajD.UK.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_UK_TajimaD_50kb.Tajima.D",sep="\t"))
TajD.AU.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_AU_TajimaD_50kb.Tajima.D",sep="\t"))
TajD.US.50kb <- as_tibble(read.csv("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/EUSTreseq_pseudochrom_US_TajimaD_50kb.Tajima.D",sep="\t"))

vcftools outputs a few identifies for position: “CHROM,” “BIN_START” and “BIN_END” for .fst and .pi files, but only “CHROM” AND “BIN_START” for .Tajima.D files. Unfortunately, the numbering is off, so we’ll add 1 to every “BIN_START” in the .Tajima.D files.

head(TajD.AU.50kb)
## # A tibble: 6 x 4
##   CHROM BIN_START N_SNPS TajimaD
##   <fct>     <int>  <int>   <dbl>
## 1 10            0    147  0.583 
## 2 10        50000    334  0.372 
## 3 10       100000    301  0.836 
## 4 10       150000     98  1.56  
## 5 10       200000    102 -0.557 
## 6 10       250000    139 -0.0356
head(fst.AUUK.50kb)
## # A tibble: 6 x 6
##   CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST  MEAN_FST
##   <fct>     <int>   <int>      <int>        <dbl>     <dbl>
## 1 10            1   50000        164      0.00792  0.0105  
## 2 10        50001  100000        379      0.0595   0.0500  
## 3 10       100001  150000        311      0.00728 -0.000459
## 4 10       150001  200000        120      0.0162   0.0275  
## 5 10       200001  250000        121      0.126    0.0926  
## 6 10       250001  300000        155      0.109    0.0747
TajD.UK.50kb$BIN_START <- TajD.UK.50kb$BIN_START + 1
TajD.AU.50kb$BIN_START <- TajD.AU.50kb$BIN_START + 1
TajD.US.50kb$BIN_START <- TajD.US.50kb$BIN_START + 1

Now BIN_START should match. To use dplyr, we’ll need a column that specifes a unique position in the genome. Create a new column that joins “CHROM” and “BIN_START” so that we match each value based on actual position in the genome.

fst.UKUS.50kb$POS_ID <- paste(fst.UKUS.50kb$CHROM,fst.UKUS.50kb$BIN_START,sep="-")
fst.AUUK.50kb$POS_ID <- paste(fst.AUUK.50kb$CHROM,fst.AUUK.50kb$BIN_START,sep="-")
fst.USAU.50kb$POS_ID <- paste(fst.USAU.50kb$CHROM,fst.USAU.50kb$BIN_START,sep="-")
pi.UK.50kb$POS_ID <- paste(pi.UK.50kb$CHROM,pi.UK.50kb$BIN_START,sep="-")
pi.AU.50kb$POS_ID <- paste(pi.AU.50kb$CHROM,pi.AU.50kb$BIN_START,sep="-")
pi.US.50kb$POS_ID <- paste(pi.US.50kb$CHROM,pi.US.50kb$BIN_START,sep="-")
TajD.UK.50kb$POS_ID <- paste(TajD.UK.50kb$CHROM,TajD.UK.50kb$BIN_START,sep="-")
TajD.AU.50kb$POS_ID <- paste(TajD.AU.50kb$CHROM,TajD.AU.50kb$BIN_START,sep="-")
TajD.US.50kb$POS_ID <- paste(TajD.US.50kb$CHROM,TajD.US.50kb$BIN_START,sep="-")

Now drop column names that we no longer need, otherwise we’ll have a giant table after all the merging.

fst.AUUK.50kb <- fst.AUUK.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
fst.USAU.50kb <- fst.USAU.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
pi.UK.50kb <- pi.UK.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
pi.AU.50kb <- pi.AU.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
pi.US.50kb <- pi.US.50kb %>% select(-CHROM, -BIN_START, -BIN_END, -N_VARIANTS)
TajD.UK.50kb <- TajD.UK.50kb %>% select(-CHROM, -BIN_START, -N_SNPS)
TajD.AU.50kb <- TajD.AU.50kb %>% select(-CHROM, -BIN_START, -N_SNPS)
TajD.US.50kb <- TajD.US.50kb %>% select(-CHROM, -BIN_START, -N_SNPS)

We’ll join tables based on the unique position identified (POS_ID). The rename() command ensures that each new column header specifies population (new name first). Heads up: each time you join two tables, those two tables are no longer accessible except as a joined table!

fstUKUS.fstAUUK.50kb <- left_join(fst.UKUS.50kb,fst.AUUK.50kb, by = "POS_ID", copy = FALSE, suffix=c("_UKUS","_AUUK"))
fst.50kb <- left_join(fstUKUS.fstAUUK.50kb,fst.USAU.50kb, by = "POS_ID", copy = FALSE, suffix=c("","_USAU"))
fst.50kb <- rename(fst.50kb, WEIGHTED_FST_USAU = WEIGHTED_FST)
fst.50kb <- rename(fst.50kb, MEAN_FST_USAU = MEAN_FST)
fst.50kb.piUK <- left_join(fst.50kb,pi.UK.50kb, by = "POS_ID", copy = FALSE)
fst.50kb.piUK.piUS <- left_join(fst.50kb.piUK,pi.US.50kb, by = "POS_ID", copy = FALSE,suffix=c("_UK","_US"))
fst.pi.50kb <- left_join(fst.50kb.piUK.piUS,pi.AU.50kb, by = "POS_ID", copy = FALSE)
fst.pi.50kb <- rename(fst.pi.50kb, PI_AU = PI)
fst.pi.50kb.TajDUK <- left_join(fst.pi.50kb,TajD.UK.50kb, by = "POS_ID", copy = FALSE)
fst.pi.50kb.TajDUK.TajDUS <- left_join(fst.pi.50kb.TajDUK,TajD.US.50kb, by = "POS_ID", copy = FALSE, suffix=c("_UK","_US"))
div <- left_join(fst.pi.50kb.TajDUK.TajDUS,TajD.AU.50kb, by = "POS_ID", copy = FALSE)
div <- rename(div, TajimaD_AU = TajimaD)

We’re going to drop data from: * small scaffolds (which conveniently start with ‘KQ’ or ‘LNCF’, * any rows (positions) with missing data (NA), * and also coerce “negative” FST or pi to zero. We replace POS_ID (which was converted to numeric) with SNP. For the qqman package, chromosomes need to be a numeric value, so we also use lapply() below to rename chromosomes.

div <- filter(div, !grepl('KQ',CHROM))
div <- filter(div, !grepl('LNCF',CHROM))
div <- filter(div, !grepl('Unknown',CHROM))
div <- div %>% drop_na()
div[,c(5:6)][div[,c(5:6)] < 0] <- 0
div[,c(8:14)][div[,c(8:14)] < 0] <- 0
div <- data.frame(lapply(div, function(x) {gsub("1A", "1.25", x)}))
div <- data.frame(lapply(div, function(x) {gsub("1B", "1.75", x)}))
div <- data.frame(lapply(div, function(x) {gsub("4A", "4.5", x)}))
div <- data.frame(lapply(div, function(x) {gsub("LG5", "28", x)}))
div <- data.frame(lapply(div, function(x) {gsub("LGE22", "29", x)}))
div <- data.frame(lapply(div, function(x) {gsub("Z", "0", x)}))
indx <- sapply(div, is.factor)
div[indx] <- lapply(div[indx], function(x) as.numeric(as.character(x)))
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
div <- div %>% select(-POS_ID)
div$SNP <- seq.int(nrow(div))
str(div)
## 'data.frame':    20071 obs. of  17 variables:
##  $ CHROM            : num  10 10 10 10 10 10 10 10 10 10 ...
##  $ BIN_START        : num  1 50001 100001 150001 200001 ...
##  $ BIN_END          : num  50000 100000 150000 200000 250000 300000 350000 400000 450000 500000 ...
##  $ N_VARIANTS       : num  165 377 311 120 121 156 80 101 24 18 ...
##  $ WEIGHTED_FST_UKUS: num  0.01745 0.03751 0 0 0.00586 ...
##  $ MEAN_FST_UKUS    : num  0.0133 0.0272 0 0 0 ...
##  $ WEIGHTED_FST_AUUK: num  0.00792 0.05948 0.00728 0.01624 0.12564 ...
##  $ MEAN_FST_AUUK    : num  0.0105 0.05 0 0.0275 0.0926 ...
##  $ WEIGHTED_FST_USAU: num  0.01291 0.00258 0 0 0.00315 ...
##  $ MEAN_FST_USAU    : num  0.0093 0.00585 0 0 0 ...
##  $ PI_UK            : num  0.001053 0.002189 0.002212 0.000915 0.000879 ...
##  $ PI_US            : num  0.001063 0.002373 0.002003 0.000829 0.0007 ...
##  $ PI_AU            : num  0.001024 0.002211 0.0022 0.000804 0.000532 ...
##  $ TajimaD_UK       : num  0.808 0.293 0.877 1.426 0.968 ...
##  $ TajimaD_US       : num  0.42348 0.58012 0.80239 1.05163 -0.00424 ...
##  $ TajimaD_AU       : num  0.583 0.372 0.836 1.557 -0.557 ...
##  $ SNP              : int  1 2 3 4 5 6 7 8 9 10 ...

Now we’re ready to plot and calculate genome-wide values!

Exploring genetic variation

First, we look at the distribution of variation across the genome.

Manhattan plots

manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.UKUS.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()
## quartz_off_screen 
##                 2
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.AUUK.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()
## quartz_off_screen 
##                 2
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_USAU", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.USAU.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_USAU", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()
## quartz_off_screen 
##                 2

Histograms of FST, pi

What’s the statistical distribution of these values?

summary(div$WEIGHTED_FST_AUUK)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.01132 0.02659 0.03258 0.04437 0.40190
summary(div$WEIGHTED_FST_UKUS)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000000 0.0001839 0.0125022 0.0187334 0.0263484 0.3414900
summary(div$WEIGHTED_FST_USAU)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.01724 0.03414 0.04038 0.05435 0.48283
lab.AU <- rep("AU.UK",length(div$WEIGHTED_FST_AUUK))
lab.US <- rep("UK.US",length(div$WEIGHTED_FST_UKUS))
Fst.group <- c(lab.AU,lab.US)
Fst.hist.data <- c(div$WEIGHTED_FST_AUUK,div$WEIGHTED_FST_USUK)
Fst.hist <- data.frame(Fst = Fst.hist.data, population = Fst.group)
pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/HistDensity_Fst.pdf",width=4,height=3)
ggplot(Fst.hist, aes(x=Fst, y=..density.., fill=population)) +
  theme_classic() +
  geom_density(alpha=0.5,lwd=0.5) +
  scale_fill_manual(values=c("#F2C14E","#2c81a8")) + xlim(0,0.5) + 
  xlab("Fst") + labs(fill="Population") + 
  geom_vline(xintercept=0.03,colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.01,colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.08,colour=alpha("gray50"),linetype="dotted", size=0.5)
dev.off()
## quartz_off_screen 
##                 2
ggplot(Fst.hist, aes(x=Fst, y=..density.., fill=population)) +
  theme_classic() +
  geom_density(alpha=0.5,lwd=0.5) +
  scale_fill_manual(values=c("#F2C14E","#2c81a8")) + xlim(0,0.5) + 
  xlab("Fst") + labs(fill="Population") + 
  geom_vline(xintercept=0.03,colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.01,colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  geom_vline(xintercept=0.08,colour=alpha("gray50"),linetype="dotted", size=0.5)

summary(div$PI_AU)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0027682 0.0040090 0.0038645 0.0050565 0.0138998
summary(div$PI_UK)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0028343 0.0041312 0.0039757 0.0051943 0.0141869
summary(div$PI_US)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 4.667e-06 2.753e-03 4.016e-03 3.863e-03 5.045e-03 1.409e-02
lab.AU <- rep("AU",length(div$PI_AU))
lab.US <- rep("US",length(div$PI_US))
lab.UK <- rep("UK",length(div$PI_UK))
group <- c(lab.AU,lab.US,lab.UK)
pi.hist.data <- c(div$PI_UK,div$PI_US,div$PI_AU)
pi.hist.lab <- data.frame(pi = pi.hist.data, population = group)
str(pi.hist.lab)
## 'data.frame':    60213 obs. of  2 variables:
##  $ pi        : num  0.001053 0.002189 0.002212 0.000915 0.000879 ...
##  $ population: Factor w/ 3 levels "AU","UK","US": 1 1 1 1 1 1 1 1 1 1 ...
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("black","#2c81a8","#F2C14E")) + xlim(-0.0001,0.02) + 
  xlab("Pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div$PI_AU),colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  geom_vline(xintercept=mean(div$PI_US),colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  theme(legend.position="none")

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/HistDensity_Pi.pdf",width=4,height=3)
ggplot(pi.hist.lab, aes(x=pi, y=..density.., fill=population)) +
  geom_density(alpha=0.8,lwd=0.5) + theme_classic() +
  scale_fill_manual(values=c("black","#2c81a8","#F2C14E")) + xlim(-0.0001,0.02) + 
  xlab("Pi") + labs(fill="Population") +
  geom_vline(xintercept=mean(div$PI_US),colour=alpha("#2c81a8"),linetype="dashed", size=1) +
  geom_vline(xintercept=mean(div$PI_AU),colour=alpha("#F2C14E"),linetype="dashed", size=1) +
  theme(legend.position="none")
dev.off()
## quartz_off_screen 
##                 2

Average nucleotide diversity for both invasions is the same (0.003). There are two vertical lines overlaid in the plot above.

ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_US),col="#2c81a8",cex=0.7) +
  xlab("") + ylab("") + xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_US),span=0.2,method="loess",col="black",lwd=0.5)
## `geom_smooth()` using formula 'y ~ x'

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Pi_USvsUK.pdf",width=2,height=2)
ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_US),col="#2c81a8",cex=0.7) +
  xlab("") + ylab("") + xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_US),span=0.2,method="loess",col="black",lwd=0.5)
## `geom_smooth()` using formula 'y ~ x'
dev.off()
## quartz_off_screen 
##                 2
ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_AU),col="#F2C14E",cex=0.7) +
  xlab("") + ylab("") +
  xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_AU),span=0.2,method="loess",col="black",lwd=0.5) 
## `geom_smooth()` using formula 'y ~ x'

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Pi_AUvsUK.pdf",width=2,height=2)
ggplot(data=div) +
  geom_point(aes(x=PI_UK, y=PI_AU),col="#F2C14E",cex=0.7) +
  xlab("") + ylab("") +
  xlim(0,0.02) + ylim(0,0.02) + theme_classic() +
  theme(axis.text=element_text(size=7,colour="black")) +
  stat_smooth(aes(x=PI_UK, y=PI_AU),span=0.2,method="loess",col="black",lwd=0.5) 
## `geom_smooth()` using formula 'y ~ x'
dev.off()
## quartz_off_screen 
##                 2

Identifying outliers

quantile(div$WEIGHTED_FST_AUUK, c(.9,.99,.999)) 
##       90%       99%     99.9% 
## 0.0676462 0.1490122 0.3077638
quantile(div$WEIGHTED_FST_UKUS, c(.9,.99,.999)) 
##       90%       99%     99.9% 
## 0.0441469 0.1156152 0.2228447
mean(div$WEIGHTED_FST_AUUK) + 5*sd(div$WEIGHTED_FST_AUUK)
## [1] 0.1936444
mean(div$WEIGHTED_FST_UKUS) + 5*sd(div$WEIGHTED_FST_UKUS)
## [1] 0.1406712
div.outliers.AUUK <- div[which(div$WEIGHTED_FST_AUUK > quantile(div$WEIGHTED_FST_AUUK,.99)),]
div.outliers.USUK <- div[which(div$WEIGHTED_FST_UKUS > quantile(div$WEIGHTED_FST_UKUS,.99)),]

unique(div.outliers.USUK$CHROM)
##  [1] 11.00 12.00 13.00 17.00 19.00  1.25  1.00 28.00  2.00  3.00  4.50  4.00
## [13]  6.00 29.00  0.00
length(div.outliers.USUK$SNP)
## [1] 201
write.csv(div.outliers.USUK,"/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/FstOutliers.USUK.csv")

unique(div.outliers.AUUK$CHROM)
##  [1] 10.00 12.00 13.00 17.00 18.00  1.25  1.00 23.00 27.00  2.00  3.00  4.50
## [13]  4.00  5.00  6.00  7.00  8.00  0.00
length(div.outliers.AUUK$SNP)
## [1] 201
write.csv(div.outliers.AUUK,"/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/FstOutliers.AUUK.csv")

What’s going on w/ other metrics at these outliers?

summary(div.outliers.AUUK$PI_UK)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0000730 0.0001890 0.0009734 0.0014188 0.0057224
summary(div.outliers.AUUK$PI_AU)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 1.383e-05 7.067e-05 2.285e-04 9.324e-04 1.447e-03 5.313e-03
summary(div.outliers.AUUK$TajimaD_AU)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -2.3050  0.2574  0.7501  0.6479  1.2528  2.6922
summary(div.outliers.AUUK$TajimaD_UK)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -1.2937  0.1481  0.6493  0.5926  1.0098  2.3588
summary(div.outliers.USUK$PI_US)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 7.167e-06 6.250e-05 2.890e-04 8.204e-04 1.281e-03 5.202e-03
summary(div.outliers.USUK$PI_UK)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000210 0.0000765 0.0002912 0.0008650 0.0012335 0.0058409
summary(div.outliers.USUK$TajimaD_US)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -2.1488 -0.4076  0.4343  0.3082  0.9422  2.9728
summary(div.outliers.USUK$TajimaD_UK)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -1.9252  0.3047  0.8963  0.8645  1.4612  2.5895

FST AU vs. US < 25% of values

div.outliers.AUUK.lowFstUSAU <- div.outliers.AUUK[which(div.outliers.AUUK$WEIGHTED_FST_USAU < quantile(div.outliers.AUUK$WEIGHTED_FST_USAU,.25) ),] 
div.outliers.AUUK.lowFstUSAU
##       CHROM BIN_START   BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 7     10.00    300001    350000         80        0.10272200    0.06002800
## 8     10.00    350001    400000        101        0.08148580    0.05463070
## 9     10.00    400001    450000         24        0.05788020    0.04084390
## 10    10.00    450001    500000         18        0.06411700    0.04388010
## 903   12.00   3500001   3550000        313        0.18415600    0.13904000
## 1413  13.00   8250001   8300000          5        0.01257140    0.02375250
## 1414  13.00   8300001   8350000          6        0.08885620    0.07648190
## 2251  17.00   2350001   2400000         90        0.07186350    0.04538140
## 2256  17.00   2600001   2650000         40        0.05519430    0.05105810
## 2257  17.00   2650001   2700000         44        0.01959680    0.00880079
## 3399   1.25  25150001  25200000         80        0.12913800    0.12104200
## 3400   1.25  25200001  25250000         65        0.07636480    0.07557310
## 3756   1.25  43000001  43050000        775        0.02825830    0.01889920
## 3793   1.25  44850001  44900000          7        0.15025500    0.15127800
## 3848   1.25  47600001  47650000        212        0.00000000    0.00000000
## 3946   1.25  52500001  52550000          9        0.10021600    0.08413560
## 4806   1.00  21450001  21500000        644        0.04794620    0.03453380
## 5603   1.00  61300001  61350000        368        0.01402780    0.01899440
## 7662  27.00   1050001   1100000         22        0.08120320    0.06346410
## 8744   2.00  43450001  43500000         11        0.20892900    0.18082200
## 9713   2.00  91900001  91950000        741        0.05756780    0.04103840
## 10693  2.00 140900001 140950000        338        0.08489620    0.07376790
## 12715  3.00  92300001  92350000        441        0.04628420    0.03479000
## 12765  3.00  94800001  94850000        560        0.09132040    0.07551770
## 13193  4.50   4800001   4850000        116        0.10282900    0.07992190
## 13204  4.50   5350001   5400000         52        0.00313758    0.00636578
## 13214  4.50   5850001   5900000         26        0.03432560    0.03319760
## 13215  4.50   5900001   5950000         19        0.09915970    0.08315180
## 13219  4.50   6100001   6150000        132        0.13811700    0.13048200
## 13220  4.50   6150001   6200000         84        0.13829600    0.13257500
## 13246  4.50   7450001   7500000        256        0.07592040    0.06709710
## 14966  5.00   2100001   2150000         88        0.03490140    0.03048680
## 16263  6.00   5400001   5450000         38        0.20652800    0.20396500
## 16265  6.00   5500001   5550000         61        0.28685900    0.28273800
## 16267  6.00   5600001   5650000         66        0.24882700    0.25382600
## 16268  6.00   5650001   5700000         90        0.26269900    0.26076500
## 16271  6.00   5800001   5850000         95        0.15699200    0.16094800
## 16272  6.00   5850001   5900000         83        0.09859130    0.10838400
## 16277  6.00   6100001   6150000        191        0.01820230    0.01385810
## 18154  8.00  26000001  26050000        413        0.04452340    0.04039470
## 18930  0.00  11400001  11450000        285        0.04931030    0.03845890
## 19171  0.00  23450001  23500000         14        0.02901440    0.03768550
## 19739  0.00  51900001  51950000         60        0.18741300    0.14925700
## 19883  0.00  59100001  59150000        335        0.08909600    0.06799050
## 19908  0.00  60350001  60400000        155        0.07248910    0.06033480
## 19962  0.00  63050001  63100000        232        0.10557800    0.06869020
## 19990  0.00  64450001  64500000        142        0.16152700    0.11803200
## 19999  0.00  64900001  64950000        186        0.17654700    0.11028100
## 20000  0.00  64950001  65000000        240        0.26377600    0.19473600
## 20002  0.00  65050001  65100000        450        0.11366200    0.09758000
##       WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 7              0.190274     0.1225760       0.000000000   0.000000000
## 8              0.245277     0.1710350       0.011134600   0.006078210
## 9              0.150496     0.1094920       0.000000000   0.000000000
## 10             0.197232     0.1555830       0.000000000   0.000000000
## 903            0.172857     0.1321760       0.000000000   0.000000000
## 1413           0.160875     0.1499450       0.041640000   0.048431800
## 1414           0.165927     0.1253930       0.004134130   0.019587600
## 2251           0.179935     0.1495680       0.000000000   0.000475237
## 2256           0.234331     0.2250580       0.062024000   0.048598500
## 2257           0.184148     0.1546670       0.057497500   0.040031300
## 3399           0.187165     0.1733300       0.000000000   0.000000000
## 3400           0.179424     0.1681800       0.000000000   0.000000000
## 3756           0.153486     0.1096770       0.047128200   0.032933000
## 3793           0.192503     0.1665460       0.008088430   0.000000000
## 3848           0.154326     0.1422630       0.033323300   0.028389200
## 3946           0.157895     0.1337510       0.000000000   0.000000000
## 4806           0.156256     0.1164610       0.053443100   0.034187400
## 5603           0.160966     0.1373110       0.022177800   0.025749000
## 7662           0.179526     0.1424830       0.061700000   0.049888500
## 8744           0.153962     0.1376650       0.038489400   0.044758200
## 9713           0.157867     0.1272150       0.038086200   0.037184400
## 10693          0.167691     0.1203220       0.022389000   0.021932000
## 12715          0.182657     0.1341990       0.036491100   0.030771200
## 12765          0.166500     0.1280040       0.060682200   0.047808400
## 13193          0.189870     0.1419040       0.028124500   0.017321500
## 13204          0.151789     0.1460660       0.054899500   0.040362400
## 13214          0.172418     0.1574540       0.011781700   0.009724970
## 13215          0.265970     0.2312490       0.062857100   0.031623200
## 13219          0.149469     0.1414180       0.000000000   0.000000000
## 13220          0.164826     0.1533640       0.000000000   0.000000000
## 13246          0.161041     0.1217310       0.056353500   0.049839700
## 14966          0.155045     0.0949995       0.049415100   0.073435900
## 16263          0.156650     0.1600790       0.045327900   0.068200900
## 16265          0.178806     0.1780510       0.000000000   0.000000000
## 16267          0.155095     0.1626050       0.000000000   0.000000000
## 16268          0.172276     0.1675040       0.000000000   0.010494800
## 16271          0.155570     0.1517520       0.004985280   0.006458370
## 16272          0.168476     0.1654050       0.028396900   0.026030400
## 16277          0.174052     0.1557960       0.031774200   0.029112900
## 18154          0.162685     0.1206440       0.036161800   0.032307400
## 18930          0.175500     0.1370410       0.037095800   0.034789400
## 19171          0.186015     0.1156630       0.028546200   0.000000000
## 19739          0.158887     0.1264000       0.006943530   0.004539620
## 19883          0.149288     0.1109620       0.051235300   0.045107200
## 19908          0.163472     0.1317920       0.014012300   0.012298300
## 19962          0.159585     0.1111950       0.000000000   0.003472230
## 19990          0.182779     0.1472250       0.027778300   0.035630900
## 19999          0.250744     0.1871510       0.004209500   0.002449910
## 20000          0.230248     0.1626550       0.000000000   0.000000000
## 20002          0.171135     0.1339330       0.000369775   0.000000000
##             PI_UK       PI_US       PI_AU TajimaD_UK TajimaD_US TajimaD_AU
## 7     5.80833e-04 3.92847e-04 2.90669e-04  1.0105400 -0.5692140  -1.333590
## 8     7.40000e-04 5.41508e-04 2.68501e-04  1.1314500 -0.3581460  -1.660240
## 9     1.45667e-04 1.08668e-04 7.26667e-05  0.6742720 -0.9384360  -1.463810
## 10    1.15667e-04 7.03333e-05 3.06667e-05  0.5109900 -1.4021400  -0.520315
## 903   1.58583e-03 2.29027e-03 2.38120e-03  0.3224690  1.2142500   1.230810
## 1413  3.40000e-05 4.31667e-05 2.45000e-05  1.2834700  1.4248800  -0.616373
## 1414  3.53333e-05 4.86667e-05 5.13333e-05 -0.0786110  2.0263200   1.435800
## 2251  5.73500e-04 5.66689e-04 5.08838e-04  0.8776000  0.3674700   0.645267
## 2256  3.06333e-04 2.34504e-04 1.60833e-04  1.8858700 -0.0331017   0.275511
## 2257  3.25835e-04 3.09179e-04 2.95838e-04  1.3731800  0.9265430   0.909031
## 3399  6.12500e-04 8.16000e-04 7.46855e-04  1.1605200  2.9728000   2.474060
## 3400  4.95167e-04 6.38006e-04 5.69339e-04  1.3865200  2.7378100   2.391710
## 3756  5.00576e-03 5.23995e-03 4.72779e-03  0.7197390  0.7382850   0.627948
## 3793  6.46667e-05 4.11669e-05 4.38335e-05  1.8770300  0.1189640   0.239407
## 3848  1.79600e-03 1.93696e-03 1.64502e-03  2.3076300  2.3360800   1.344600
## 3946  7.31667e-05 6.43336e-05 5.60000e-05  1.2810300  0.7658780   0.581803
## 4806  3.95939e-03 4.03464e-03 4.02009e-03  0.6895930  0.4131160   0.683000
## 5603  2.00387e-03 2.55315e-03 2.40114e-03  0.5395780  0.9626730   1.158770
## 7662  1.32833e-04 1.42505e-04 1.66336e-04  0.4596240  1.4228800   1.391460
## 8744  8.03333e-05 7.95000e-05 1.07667e-04  1.2412800  1.7098300   1.543140
## 9713  4.20641e-03 5.01278e-03 4.71548e-03  0.4723270  0.7212530   1.020770
## 10693 1.78185e-03 2.45788e-03 2.23679e-03  0.1567670  1.1972700   0.964910
## 12715 2.86887e-03 2.95765e-03 2.46995e-03  0.9001180  0.7614710   0.571869
## 12765 3.61187e-03 3.45875e-03 3.34935e-03  0.7481670  0.8393080   0.898908
## 13193 8.40333e-04 7.80186e-04 6.83016e-04  1.2913500  0.5936570   0.246120
## 13204 3.65668e-04 2.43669e-04 8.05000e-05  0.9323710 -0.8942190   0.105573
## 13214 1.98167e-04 1.05000e-04 3.31668e-05  1.0810900 -1.6154100  -0.836140
## 13215 1.56667e-04 5.98333e-05 1.38333e-05  1.4741500 -1.7879600   0.377685
## 13219 1.16983e-03 4.09190e-04 3.88846e-04  2.1269100 -2.1488000  -2.179840
## 13220 7.55674e-04 2.66003e-04 2.38502e-04  2.1548000 -2.0226400  -2.304990
## 13246 1.33183e-03 1.45129e-03 1.21154e-03  0.5049100  0.2272260   0.171851
## 14966 4.34167e-04 7.43677e-04 4.98838e-04 -0.6845970  1.8217500   2.692190
## 16263 3.41667e-05 2.85168e-04 2.28500e-04  0.4407030  1.0635900   1.090820
## 16265 2.88333e-05 5.33346e-04 4.75333e-04  1.7237100  2.0565000   1.339670
## 16267 5.08333e-05 5.92009e-04 5.25841e-04  0.7217460  2.1616400   1.510490
## 16268 6.93333e-05 7.88034e-04 6.67837e-04 -1.2936700  2.1362700   1.375800
## 16271 1.58333e-04 6.64365e-04 7.56515e-04 -0.1743870  1.1296400   1.427160
## 16272 1.28833e-04 5.18515e-04 8.11345e-04  0.2616560  0.2983540   1.422590
## 16277 1.07350e-03 1.16373e-03 1.16603e-03  0.4774840  0.1391540   1.368240
## 18154 2.51487e-03 2.77048e-03 2.48267e-03  0.6647450  0.9376180   0.861756
## 18930 1.83451e-03 1.94116e-03 1.38422e-03  0.7745260  0.7981410   0.527410
## 19171 2.45000e-05 7.31667e-05 9.48338e-05 -0.0665776 -0.9034210   0.440062
## 19739 3.98671e-04 3.79014e-04 4.32010e-04  1.2068700  0.9307090   1.050540
## 19883 2.27434e-03 2.39084e-03 1.94756e-03  0.7192310  1.0710000   0.619528
## 19908 1.11083e-03 1.11738e-03 9.21373e-04  1.4358300  0.9503040   0.471944
## 19962 1.55317e-03 1.37795e-03 1.14304e-03  0.6403910  0.5738490   0.685643
## 19990 6.87667e-04 1.12640e-03 1.36371e-03 -0.5452820  1.6366100   1.798960
## 19999 8.16667e-04 1.26139e-03 1.36611e-03 -0.2434000  0.6359220   1.156450
## 20000 1.24650e-03 1.72167e-03 1.74439e-03 -0.2072230  1.1044000   1.037880
## 20002 2.43118e-03 3.10507e-03 2.86397e-03  0.9240930  0.7368680   0.386692
##         SNP
## 7         7
## 8         8
## 9         9
## 10       10
## 903     903
## 1413   1413
## 1414   1414
## 2251   2251
## 2256   2256
## 2257   2257
## 3399   3399
## 3400   3400
## 3756   3756
## 3793   3793
## 3848   3848
## 3946   3946
## 4806   4806
## 5603   5603
## 7662   7662
## 8744   8744
## 9713   9713
## 10693 10693
## 12715 12715
## 12765 12765
## 13193 13193
## 13204 13204
## 13214 13214
## 13215 13215
## 13219 13219
## 13220 13220
## 13246 13246
## 14966 14966
## 16263 16263
## 16265 16265
## 16267 16267
## 16268 16268
## 16271 16271
## 16272 16272
## 16277 16277
## 18154 18154
## 18930 18930
## 19171 19171
## 19739 19739
## 19883 19883
## 19908 19908
## 19962 19962
## 19990 19990
## 19999 19999
## 20000 20000
## 20002 20002
length(div.outliers.AUUK.lowFstUSAU$SNP)
## [1] 50
div.outliers.USUK.lowFstUSAU <- div.outliers.USUK[which(div.outliers.USUK$WEIGHTED_FST_USAU < quantile(div.outliers.AUUK$WEIGHTED_FST_USAU,.25) ),] 
div.outliers.USUK.lowFstUSAU
##       CHROM BIN_START   BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 903   12.00   3500001   3550000        313          0.184156     0.1390400
## 3399   1.25  25150001  25200000         80          0.129138     0.1210420
## 3604   1.25  35400001  35450000        267          0.170100     0.1207920
## 3761   1.25  43250001  43300000          6          0.125362     0.0777105
## 3770   1.25  43700001  43750000          6          0.224692     0.1937840
## 3772   1.25  43800001  43850000          9          0.233478     0.2201170
## 3773   1.25  43850001  43900000          9          0.164466     0.1563530
## 3774   1.25  43900001  43950000          8          0.222953     0.2224730
## 3775   1.25  43950001  44000000         12          0.146320     0.1270320
## 3778   1.25  44100001  44150000          5          0.237337     0.2359130
## 3781   1.25  44250001  44300000          8          0.198336     0.1773510
## 3783   1.25  44350001  44400000          2          0.241218     0.2370690
## 3784   1.25  44400001  44450000          8          0.150582     0.1343090
## 3788   1.25  44600001  44650000          8          0.183673     0.1641710
## 3790   1.25  44700001  44750000          9          0.198634     0.1724300
## 3791   1.25  44750001  44800000          4          0.126050     0.1050630
## 3792   1.25  44800001  44850000          5          0.158329     0.1520360
## 3793   1.25  44850001  44900000          7          0.150255     0.1512780
## 3798   1.25  45100001  45150000          6          0.168067     0.1630950
## 3806   1.25  45500001  45550000          9          0.134111     0.1168480
## 3826   1.25  46500001  46550000         16          0.119742     0.1104310
## 3941   1.25  52250001  52300000          6          0.144998     0.1072330
## 3944   1.25  52400001  52450000          7          0.149580     0.1337060
## 3945   1.25  52450001  52500000          3          0.151211     0.1466990
## 3949   1.25  52650001  52700000          7          0.135316     0.1076190
## 3950   1.25  52700001  52750000          3          0.122153     0.0888192
## 3952   1.25  52800001  52850000          7          0.165469     0.1079770
## 4645   1.00  13400001  13450000        424          0.122010     0.1080020
## 4650   1.00  13650001  13700000        109          0.124589     0.1162740
## 4775   1.00  19900001  19950000        300          0.135622     0.0912465
## 6505   1.00 106400001 106450000         36          0.121953     0.1074120
## 6508   1.00 106550001 106600000         37          0.155452     0.1421030
## 6510   1.00 106650001 106700000         39          0.133155     0.1137390
## 6513   1.00 106800001 106850000         48          0.188133     0.1713330
## 6514   1.00 106850001 106900000         40          0.132203     0.1245470
## 6515   1.00 106900001 106950000         45          0.232680     0.1886790
## 6523   1.00 107300001 107350000         30          0.268736     0.1772350
## 6524   1.00 107350001 107400000         34          0.218703     0.1623010
## 6539   1.00 108100001 108150000         24          0.167678     0.1600050
## 6541   1.00 108200001 108250000         38          0.128295     0.1078590
## 6543   1.00 108300001 108350000         48          0.142444     0.1297560
## 6544   1.00 108350001 108400000         34          0.125889     0.1123100
## 6545   1.00 108400001 108450000         39          0.126228     0.1090190
## 6548   1.00 108550001 108600000         55          0.118252     0.1023220
## 6576   1.00 109950001 110000000         21          0.147682     0.1261950
## 6577   1.00 110000001 110050000         34          0.145129     0.1138110
## 6578   1.00 110050001 110100000         36          0.161099     0.1357970
## 6579   1.00 110100001 110150000         35          0.150256     0.1007880
## 7820  28.00   3100001   3150000        260          0.128025     0.0905937
## 8743   2.00  43400001  43450000         15          0.163207     0.1107770
## 8744   2.00  43450001  43500000         11          0.208929     0.1808220
## 9442   2.00  78350001  78400000        494          0.128228     0.0907695
## 9443   2.00  78400001  78450000        574          0.162178     0.1107060
## 10488  2.00 130650001 130700000        355          0.221406     0.1577580
## 11075  3.00  10150001  10200000        544          0.117170     0.0844310
## 12402  3.00  76550001  76600000        435          0.140209     0.1053910
## 12481  3.00  80500001  80550000        339          0.130490     0.1016480
## 13043  3.00 108700001 108750000        282          0.137572     0.1262160
## 13050  3.00 109050001 109100000        294          0.131216     0.1117980
## 13217  4.50   6000001   6050000         87          0.127251     0.1166620
## 13218  4.50   6050001   6100000        122          0.124097     0.1208020
## 13219  4.50   6100001   6150000        132          0.138117     0.1304820
## 13220  4.50   6150001   6200000         84          0.138296     0.1325750
## 13949  4.00  22200001  22250000         15          0.146000     0.0954939
## 13950  4.00  22250001  22300000         10          0.134546     0.0918042
## 13951  4.00  22300001  22350000         10          0.174495     0.1590860
## 13953  4.00  22400001  22450000         15          0.135206     0.0962744
## 13976  4.00  23550001  23600000         19          0.119635     0.0882177
## 13977  4.00  23600001  23650000          8          0.126522     0.1102040
## 13999  4.00  24700001  24750000         39          0.150117     0.1263990
## 14001  4.00  24800001  24850000         33          0.127962     0.1023800
## 14057  4.00  27600001  27650000         43          0.124484     0.1000850
## 14058  4.00  27650001  27700000         15          0.124772     0.1110270
## 14243  4.00  36900001  36950000        120          0.144461     0.1311450
## 16262  6.00   5350001   5400000         23          0.152651     0.1426320
## 16263  6.00   5400001   5450000         38          0.206528     0.2039650
## 16265  6.00   5500001   5550000         61          0.286859     0.2827380
## 16266  6.00   5550001   5600000         76          0.236749     0.2467770
## 16267  6.00   5600001   5650000         66          0.248827     0.2538260
## 16268  6.00   5650001   5700000         90          0.262699     0.2607650
## 16269  6.00   5700001   5750000        118          0.200669     0.1998990
## 16270  6.00   5750001   5800000         84          0.189015     0.1918170
## 16271  6.00   5800001   5850000         95          0.156992     0.1609480
## 19081  0.00  18950001  19000000        222          0.189965     0.1459400
## 19082  0.00  19000001  19050000        193          0.145196     0.1232680
## 19554  0.00  42650001  42700000        126          0.174597     0.1252560
## 19555  0.00  42700001  42750000        148          0.144410     0.1008920
## 19560  0.00  42950001  43000000        513          0.132871     0.0928841
## 19739  0.00  51900001  51950000         60          0.187413     0.1492570
## 19749  0.00  52400001  52450000        158          0.116486     0.1179690
## 19773  0.00  53600001  53650000        416          0.136896     0.1041320
## 19789  0.00  54400001  54450000        216          0.163008     0.1295300
## 19800  0.00  54950001  55000000        332          0.138968     0.1067000
## 19801  0.00  55000001  55050000        507          0.154670     0.1256850
## 19827  0.00  56300001  56350000        413          0.183956     0.1316770
## 19910  0.00  60450001  60500000        201          0.118053     0.0861133
## 19932  0.00  61550001  61600000        113          0.165185     0.1225240
## 19952  0.00  62550001  62600000        170          0.118434     0.0547930
## 19990  0.00  64450001  64500000        142          0.161527     0.1180320
## 19999  0.00  64900001  64950000        186          0.176547     0.1102810
## 20000  0.00  64950001  65000000        240          0.263776     0.1947360
## 20001  0.00  65000001  65050000        277          0.128128     0.0954193
## 20004  0.00  65150001  65200000        431          0.217513     0.1791900
## 20006  0.00  65250001  65300000        194          0.175280     0.1503040
##       WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 903          0.17285700    0.13217600        0.00000000    0.00000000
## 3399         0.18716500    0.17333000        0.00000000    0.00000000
## 3604         0.07934410    0.07020960        0.05587520    0.03937620
## 3761         0.00000000    0.00000000        0.05148780    0.06184870
## 3770         0.03718200    0.02433470        0.05817800    0.03405610
## 3772         0.13561700    0.13064000        0.00000000    0.00000000
## 3773         0.08522380    0.07655750        0.00000000    0.00000000
## 3774         0.13353400    0.13178400        0.00000000    0.00000000
## 3775         0.08134920    0.07170890        0.02982870    0.02672950
## 3778         0.14647400    0.15034100        0.01565280    0.00122076
## 3781         0.11779400    0.10369500        0.00646088    0.00000000
## 3783         0.09485710    0.09192040        0.02521010    0.00846979
## 3784         0.10530600    0.09744460        0.05184410    0.05553860
## 3788         0.11779400    0.10369500        0.00209059    0.00000000
## 3790         0.11815600    0.10626900        0.00000000    0.00000000
## 3791         0.10878100    0.08895970        0.00000000    0.00000000
## 3792         0.10128700    0.10070000        0.00000000    0.00000000
## 3793         0.19250300    0.16654600        0.00808843    0.00000000
## 3798         0.03961580    0.03907690        0.04439060    0.03577230
## 3806         0.06783720    0.05030530        0.04633200    0.04930240
## 3826         0.13793800    0.13914200        0.00000000    0.00899771
## 3941         0.08532940    0.05885460        0.00000000    0.00000000
## 3944         0.08503400    0.07736640        0.05946680    0.02578540
## 3945         0.09730850    0.09256980        0.05668930    0.06439390
## 3949         0.08662550    0.06917960        0.02782180    0.00000000
## 3950         0.07783250    0.05390090        0.00000000    0.00000000
## 3952         0.13227500    0.11799700        0.03095210    0.03397320
## 4645         0.07544880    0.04780920        0.04652650    0.03611070
## 4650         0.13459400    0.11253100        0.01055670    0.01236400
## 4775         0.04407340    0.02756790        0.03553260    0.02405860
## 6505         0.02867540    0.02965140        0.01655830    0.01288510
## 6508         0.01050260    0.01209770        0.04161900    0.03951340
## 6510         0.00000000    0.00000000        0.02712230    0.02564460
## 6513         0.08465690    0.08395070        0.01655570    0.01467120
## 6514         0.11206500    0.10212800        0.00000000    0.00000000
## 6515         0.04880760    0.04785990        0.05205620    0.04430950
## 6523         0.04707790    0.02864420        0.03456880    0.01504170
## 6524         0.02390470    0.02194430        0.03444050    0.02495470
## 6539         0.00000000    0.00000000        0.05837130    0.05240620
## 6541         0.00000000    0.00000000        0.05655660    0.05103650
## 6543         0.00000000    0.00000000        0.05827780    0.05722940
## 6544         0.00000000    0.00000000        0.05258910    0.05286880
## 6545         0.00000000    0.00000000        0.05034720    0.05486100
## 6548         0.00124640    0.00655647        0.05921450    0.06257130
## 6576         0.00000000    0.00000000        0.01856180    0.02699640
## 6577         0.00000000    0.00000000        0.03976700    0.04051910
## 6578         0.00000000    0.00000000        0.03371040    0.04170340
## 6579         0.00700709    0.00000000        0.03957400    0.04957580
## 7820         0.12573300    0.08601820        0.03825490    0.02761940
## 8743         0.13011800    0.08951140        0.03829720    0.05255340
## 8744         0.15396200    0.13766500        0.03848940    0.04475820
## 9442         0.00757870    0.01085680        0.04390340    0.03497290
## 9443         0.04318200    0.04200010        0.06064170    0.04349010
## 10488        0.04573520    0.03052990        0.04144430    0.03024190
## 11075        0.01262000    0.00786930        0.03573090    0.02699290
## 12402        0.02884220    0.01682680        0.05337690    0.04493240
## 12481        0.01170780    0.00827685        0.02750570    0.01481100
## 13043        0.04108050    0.02927340        0.03344080    0.04481170
## 13050        0.04146820    0.03504200        0.03506970    0.02772930
## 13217        0.12353500    0.11251100        0.00000000    0.00000000
## 13218        0.14078200    0.12998400        0.00000000    0.00000000
## 13219        0.14946900    0.14141800        0.00000000    0.00000000
## 13220        0.16482600    0.15336400        0.00000000    0.00000000
## 13949        0.00000000    0.00000000        0.06352870    0.05377600
## 13950        0.00000000    0.00000000        0.05085180    0.05315840
## 13951        0.01770960    0.02164560        0.06004140    0.05237340
## 13953        0.00000000    0.00000000        0.04074450    0.02970290
## 13976        0.00000000    0.00000000        0.04142370    0.03950400
## 13977        0.00000000    0.00000000        0.04691990    0.04621240
## 13999        0.09507780    0.07799270        0.05936040    0.02300210
## 14001        0.12446300    0.10075100        0.04695910    0.01768800
## 14057        0.04217290    0.03686610        0.00000000    0.00000000
## 14058        0.07080940    0.05441100        0.00000000    0.00000000
## 14243        0.03965910    0.03218780        0.00000000    0.00000000
## 16262        0.11950500    0.12077700        0.00000000    0.00000000
## 16263        0.15665000    0.16007900        0.04532790    0.06820090
## 16265        0.17880600    0.17805100        0.00000000    0.00000000
## 16266        0.14191300    0.15019900        0.00000000    0.00000000
## 16267        0.15509500    0.16260500        0.00000000    0.00000000
## 16268        0.17227600    0.16750400        0.00000000    0.01049480
## 16269        0.14281100    0.14575800        0.00000000    0.00000000
## 16270        0.14529700    0.15022400        0.00000000    0.00000000
## 16271        0.15557000    0.15175200        0.00498528    0.00645837
## 19081        0.07504440    0.05388810        0.00000000    0.00000000
## 19082        0.01930020    0.02679270        0.00000000    0.00000000
## 19554        0.03798840    0.02447900        0.03540840    0.02809860
## 19555        0.03812590    0.03245780        0.06140780    0.06038380
## 19560        0.05299900    0.04261110        0.02616120    0.02108070
## 19739        0.15888700    0.12640000        0.00694353    0.00453962
## 19749        0.11754200    0.11478100        0.05403680    0.04530590
## 19773        0.03640580    0.02289210        0.04639240    0.04121640
## 19789        0.01432970    0.00727728        0.05343340    0.03196350
## 19800        0.07911490    0.06664680        0.00000000    0.00000000
## 19801        0.12485400    0.08926860        0.01174210    0.01531360
## 19827        0.05691840    0.05319140        0.06059090    0.04912670
## 19910        0.10860800    0.07455150        0.04992710    0.03989120
## 19932        0.08338520    0.05801270        0.00000000    0.00000000
## 19952        0.11411500    0.07473020        0.06234610    0.05415480
## 19990        0.18277900    0.14722500        0.02777830    0.03563090
## 19999        0.25074400    0.18715100        0.00420950    0.00244991
## 20000        0.23024800    0.16265500        0.00000000    0.00000000
## 20001        0.13011500    0.10417600        0.00000000    0.00000000
## 20004        0.13823800    0.12733400        0.04345990    0.03005140
## 20006        0.12161100    0.10622800        0.00000000    0.00000000
##             PI_UK       PI_US       PI_AU TajimaD_UK TajimaD_US TajimaD_AU
## 903   1.58583e-03 2.29027e-03 2.38120e-03  0.3224690  1.2142500  1.2308100
## 3399  6.12500e-04 8.16000e-04 7.46855e-04  1.1605200  2.9728000  2.4740600
## 3604  1.44051e-03 1.45978e-03 1.50157e-03  0.5135870  0.2771000  0.4159450
## 3761  3.55002e-05 1.53335e-05 4.26669e-05  0.0530632 -0.7283210  0.8086610
## 3770  4.86667e-05 1.83335e-05 3.75000e-05  2.0263200 -1.5621200  0.1264660
## 3772  9.01667e-05 2.65000e-05 4.65000e-05  2.4320200 -1.8785200 -0.5244280
## 3773  8.30000e-05 4.03333e-05 4.91667e-05  1.9468000 -0.9419410 -0.3438820
## 3774  8.16667e-05 2.76668e-05 4.20002e-05  2.5008300 -1.3535000 -0.3733890
## 3775  9.90000e-05 6.00003e-05 6.43333e-05  1.4087000 -0.2281940  0.2512360
## 3778  5.20000e-05 1.21667e-05 3.13333e-05  2.3908300 -1.8925300 -0.4572150
## 3781  7.41667e-05 2.76667e-05 3.45000e-05  1.9400800 -1.5366100 -0.6423260
## 3783  2.11667e-05 7.16667e-06 1.38333e-05  1.9348200 -1.0378900  0.3776850
## 3784  6.15000e-05 5.08336e-05 3.53333e-05  0.9930240  0.2870570  0.5682810
## 3788  7.41667e-05 2.98333e-05 3.45000e-05  1.9400800 -1.3746100 -0.6423260
## 3790  8.61667e-05 2.65001e-05 3.85002e-05  2.1612000 -1.8336200 -0.6350750
## 3791  3.55000e-05 1.96667e-05 2.45000e-05  1.4781100 -0.5764880  0.0507059
## 3792  4.75000e-05 2.45000e-05 3.60003e-05  1.8987400 -0.6163730  0.2162880
## 3793  6.46667e-05 4.11669e-05 4.38335e-05  1.8770300  0.1189640  0.2394070
## 3798  4.46667e-05 2.60000e-05 2.63333e-05  1.5889100 -0.9620190 -0.4158930
## 3806  6.10000e-05 4.55000e-05 4.36667e-05  0.9556400 -0.5921330  0.1232250
## 3826  1.56500e-04 1.14501e-04 1.17334e-04  2.4557100  0.8907940  1.0316700
## 3941  4.66669e-05 1.55001e-05 2.20002e-05  1.1100000 -1.3170400 -0.6062470
## 3944  6.13333e-05 2.81667e-05 3.38333e-05  1.5986500 -1.1712500 -0.2205870
## 3945  2.31667e-05 2.21667e-05 1.53333e-05  0.8152060  0.6548820  0.6961910
## 3949  6.30000e-05 2.76667e-05 3.76668e-05  1.7378400 -1.2130100 -0.2756000
## 3950  2.56667e-05 1.18333e-05 1.58333e-05  1.2160200 -1.0018000 -0.3605060
## 3952  4.71667e-05 2.81668e-05 5.50000e-05  1.0414200 -0.6941640  0.5070360
## 4645  2.02018e-03 2.21849e-03 2.02657e-03  0.2700840  0.5678900 -0.4854670
## 4650  3.90335e-04 7.81508e-04 7.16844e-04 -0.2150280  1.1457000  1.0612200
## 4775  1.84085e-03 1.86902e-03 1.99326e-03  0.6239960  0.4886960  0.5699950
## 6505  2.15833e-04 3.10174e-04 2.97335e-04 -0.1306570  1.9180200  1.9323100
## 6508  2.00667e-04 2.85840e-04 2.61336e-04 -0.4681010  1.8898600  1.6874300
## 6510  2.06501e-04 3.24838e-04 2.87837e-04 -0.5397670  1.7971400  1.8105600
## 6513  2.88000e-04 3.92011e-04 4.90002e-04 -0.0912449  1.7940100  2.9641900
## 6514  2.03833e-04 3.69672e-04 3.52672e-04 -0.5570740  2.3282800  2.7553400
## 6515  2.88000e-04 2.86179e-04 3.54007e-04  0.6951460  0.7094240  1.9753400
## 6523  1.85833e-04 1.88834e-04 2.27667e-04  0.1147080  0.2218600  1.4378900
## 6524  2.51000e-04 2.12339e-04 2.77167e-04  0.8766980  0.3338670  2.0755700
## 6539  2.36500e-04 1.42835e-04 2.19001e-04  2.5894800  0.0306194  2.4172400
## 6541  3.11168e-04 2.20171e-04 3.06667e-04  1.5240500  0.0796701  2.0677500
## 6543  4.20833e-04 3.07844e-04 3.97171e-04  1.9225400  0.4146910  2.4745300
## 6544  2.96500e-04 2.18501e-04 2.88335e-04  1.8611200  0.4449630  2.3019900
## 6545  3.33500e-04 2.28343e-04 3.14167e-04  1.6753700  0.3355010  2.3956200
## 6548  4.21667e-04 3.59840e-04 4.08343e-04  1.1831300  0.5061900  2.4397700
## 6576  1.64000e-04 1.16334e-04 1.61002e-04  1.6592200 -0.4101390  1.4508200
## 6577  2.40001e-04 2.01668e-04 2.41168e-04  1.2623300 -0.1652680  1.6219800
## 6578  2.64333e-04 2.20004e-04 2.54672e-04  1.4377400  0.3525150  1.8605900
## 6579  2.12170e-04 2.43671e-04 2.43167e-04  0.1135810  1.2188200  1.7073800
## 7820  1.67667e-03 1.56360e-03 1.57443e-03  0.6311410  0.7211370  0.5805580
## 8743  7.61667e-05 9.76667e-05 9.28333e-05 -0.1077110  0.9505020  1.5119600
## 8744  8.03333e-05 7.95000e-05 1.07667e-04  1.2412800  1.7098300  1.5431400
## 9442  3.40203e-03 2.64765e-03 3.45928e-03  1.1197600  0.1691960  0.9883630
## 9443  3.48055e-03 3.02106e-03 3.46268e-03  0.2383720  0.2363800  0.9347480
## 10488 2.61183e-03 2.05047e-03 2.84127e-03  1.0329500  0.2035200  1.4815000
## 11075 3.78114e-03 3.24736e-03 3.88510e-03  0.9823450  0.4921300  1.1042900
## 12402 3.08791e-03 2.26729e-03 3.06395e-03  1.0883500  0.3555760  0.9242140
## 12481 2.65517e-03 1.82640e-03 2.40765e-03  1.4611700 -0.1302060  0.7877830
## 13043 1.80500e-03 6.78043e-04 1.36702e-03  0.5273160 -0.2477840 -0.1848460
## 13050 1.88750e-03 1.74779e-03 1.84893e-03  0.9289410  0.6593700  0.6770270
## 13217 7.23833e-04 2.57341e-04 3.00170e-04  1.7752500 -2.0753400 -1.8571700
## 13218 1.04117e-03 4.05516e-04 3.68677e-04  2.0071800 -1.9104900 -2.1351400
## 13219 1.16983e-03 4.09190e-04 3.88846e-04  2.1269100 -2.1488000 -2.1798400
## 13220 7.55674e-04 2.66003e-04 2.38502e-04  2.1548000 -2.0226400 -2.3049900
## 13949 8.88333e-05 6.73337e-05 1.03667e-04  0.2051240 -0.1991080  0.9827730
## 13950 4.75000e-05 3.91670e-05 5.78333e-05 -0.4567230  0.6802060  0.2428930
## 13951 6.86667e-05 8.25000e-05 7.80000e-05  0.5193720  1.9129500  1.0968900
## 13953 8.76667e-05 6.73337e-05 9.10009e-05  0.1513580  0.5915620  0.4389990
## 13976 9.95000e-05 8.76671e-05 1.22835e-04  0.1252330  0.9421640  0.3925180
## 13977 5.60000e-05 3.85002e-05 5.25000e-05  0.5818030  1.1879500  0.8609370
## 13999 3.24835e-04 1.60001e-04 2.16335e-04  1.7308000 -1.0120200  0.0151970
## 14001 2.48667e-04 1.70168e-04 1.46667e-04  1.3712200 -0.4382650 -0.6486470
## 14057 3.81000e-04 3.27002e-04 3.94502e-04  2.1245000  1.2611100  2.2358600
## 14058 1.20500e-04 9.08347e-05 1.06668e-04  1.3037800  1.3465400  1.2527000
## 14243 1.05652e-03 8.72538e-04 9.60703e-04  2.0220600  1.0514400  1.8017000
## 16262 5.05000e-05 1.48335e-04 1.75833e-04 -0.2536090  0.3639750  0.8790340
## 16263 3.41667e-05 2.85168e-04 2.28500e-04  0.4407030  1.0635900  1.0908200
## 16265 2.88333e-05 5.33346e-04 4.75333e-04  1.7237100  2.0565000  1.3396700
## 16266 9.40005e-05 6.90347e-04 6.21012e-04  0.1897620  2.2269900  1.4170800
## 16267 5.08333e-05 5.92009e-04 5.25841e-04  0.7217460  2.1616400  1.5104900
## 16268 6.93333e-05 7.88034e-04 6.67837e-04 -1.2936700  2.1362700  1.3758000
## 16269 1.06667e-04 9.61546e-04 9.50343e-04 -1.9252200  1.6276500  1.3995400
## 16270 7.71667e-05 6.45525e-04 6.42512e-04 -0.6821270  1.3935400  1.2760900
## 16271 1.58333e-04 6.64365e-04 7.56515e-04 -0.1743870  1.1296400  1.4271600
## 19081 1.18901e-03 1.24461e-03 1.22436e-03  0.8627480  0.7580270  0.7976620
## 19082 1.11667e-03 1.30971e-03 1.30786e-03  2.0294900  0.7847970  1.0812900
## 19554 7.09000e-04 7.83848e-04 8.43520e-04 -0.0829929  0.7339730  0.4229450
## 19555 1.02717e-03 7.53859e-04 1.12656e-03  0.9574110 -0.0083882  1.3115400
## 19560 3.19738e-03 3.03820e-03 3.53474e-03  0.8618190  0.7170390  0.7675460
## 19739 3.98671e-04 3.79014e-04 4.32010e-04  1.2068700  0.9307090  1.0505400
## 19749 1.27151e-03 8.24353e-04 9.52532e-04  1.5306200  1.8469300  1.5679700
## 19773 2.51619e-03 2.53218e-03 2.79579e-03  0.8674350  0.8165160  0.9470750
## 19789 1.75800e-03 1.27827e-03 1.65853e-03  1.7743900  0.3270870  1.2816400
## 19800 2.37000e-03 1.99995e-03 2.30563e-03  1.2463900  0.7488360  1.2112400
## 19801 2.50339e-03 3.70097e-03 3.26430e-03  0.4482920  1.4712100  0.6027790
## 19827 2.63250e-03 2.64996e-03 2.97130e-03  0.8925070  0.7706030  1.2067300
## 19910 1.06800e-03 1.39080e-03 1.21740e-03  0.1475660  0.7631470  0.1611300
## 19932 6.72170e-04 8.29064e-04 8.34358e-04  0.5027830  1.0716200  1.1047700
## 19952 8.13500e-04 8.83538e-04 1.13838e-03 -0.7025570  0.0822429  1.0951500
## 19990 6.87667e-04 1.12640e-03 1.36371e-03 -0.5452820  1.6366100  1.7989600
## 19999 8.16667e-04 1.26139e-03 1.36611e-03 -0.2434000  0.6359220  1.1564500
## 20000 1.24650e-03 1.72167e-03 1.74439e-03 -0.2072230  1.1044000  1.0378800
## 20001 1.53219e-03 1.96551e-03 2.01766e-03  0.3047390  1.1231200  1.3799400
## 20004 3.53890e-03 1.77709e-03 2.38962e-03  1.7335800 -0.4075590  1.2845000
## 20006 1.57217e-03 1.30438e-03 1.47139e-03  1.5303900  1.0990900  1.1772900
##         SNP
## 903     903
## 3399   3399
## 3604   3604
## 3761   3761
## 3770   3770
## 3772   3772
## 3773   3773
## 3774   3774
## 3775   3775
## 3778   3778
## 3781   3781
## 3783   3783
## 3784   3784
## 3788   3788
## 3790   3790
## 3791   3791
## 3792   3792
## 3793   3793
## 3798   3798
## 3806   3806
## 3826   3826
## 3941   3941
## 3944   3944
## 3945   3945
## 3949   3949
## 3950   3950
## 3952   3952
## 4645   4645
## 4650   4650
## 4775   4775
## 6505   6505
## 6508   6508
## 6510   6510
## 6513   6513
## 6514   6514
## 6515   6515
## 6523   6523
## 6524   6524
## 6539   6539
## 6541   6541
## 6543   6543
## 6544   6544
## 6545   6545
## 6548   6548
## 6576   6576
## 6577   6577
## 6578   6578
## 6579   6579
## 7820   7820
## 8743   8743
## 8744   8744
## 9442   9442
## 9443   9443
## 10488 10488
## 11075 11075
## 12402 12402
## 12481 12481
## 13043 13043
## 13050 13050
## 13217 13217
## 13218 13218
## 13219 13219
## 13220 13220
## 13949 13949
## 13950 13950
## 13951 13951
## 13953 13953
## 13976 13976
## 13977 13977
## 13999 13999
## 14001 14001
## 14057 14057
## 14058 14058
## 14243 14243
## 16262 16262
## 16263 16263
## 16265 16265
## 16266 16266
## 16267 16267
## 16268 16268
## 16269 16269
## 16270 16270
## 16271 16271
## 19081 19081
## 19082 19082
## 19554 19554
## 19555 19555
## 19560 19560
## 19739 19739
## 19749 19749
## 19773 19773
## 19789 19789
## 19800 19800
## 19801 19801
## 19827 19827
## 19910 19910
## 19932 19932
## 19952 19952
## 19990 19990
## 19999 19999
## 20000 20000
## 20001 20001
## 20004 20004
## 20006 20006
length(div.outliers.USUK.lowFstUSAU$SNP)
## [1] 104
intersect(div.outliers.AUUK.lowFstUSAU$CHROM,div.outliers.AUUK.lowFstUSAU$CHROM)
##  [1] 10.00 12.00 13.00 17.00  1.25  1.00 27.00  2.00  3.00  4.50  5.00  6.00
## [13]  8.00  0.00

Step 4: How do Fst and pi change across the genome? among invasions?

What’s the difference in diversity between native and invasive ranges?

div$piUK.piAU <- div$PI_UK - div$PI_AU
div$piUK.piUS <- div$PI_UK - div$PI_US
summary(div$piUK.piAU)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -1.552e-03 -1.935e-05  9.278e-05  1.112e-04  2.319e-04  2.028e-03
summary(div$piUK.piUS)
##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -1.381e-03 -2.535e-06  1.035e-04  1.132e-04  2.277e-04  1.762e-03

Novel diversity in invasions

In a given window, if diversity in the invasive range is higher than that of the native range, it is possible that those variants are novel mutations. This filtering will tell us whether we should look at particular genotypes in these regions.

Possibly “novel” diversity (e.g., lower than average native diversity and higher than average invasive)

div.lownatpi <- div[which(div$PI_UK < mean(div$PI_UK)),]
length(div.lownatpi$SNP)
## [1] 9337
unique(div.lownatpi$CHROM)
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00  1.25  1.75
## [13]  1.00 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00
## [25]  4.50  4.00  5.00  6.00  7.00  8.00  9.00 29.00  0.00
length(unique(div.lownatpi$CHROM))
## [1] 33

5521 SNPs have below-average diversity in the native range. Of these SNPs, how many are in the top 25% of pi in the invasive range (AU or US)?

summary(div.lownatpi$PI_AU)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000025 0.0015566 0.0026495 0.0023695 0.0033432 0.0052840
div.novelAUpi <- div.lownatpi[which(div.lownatpi$PI_AU > quantile(div.lownatpi$PI_AU,0.75)),]
unique(div.novelAUpi$CHROM) # which chromosomes 
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00  0.00
length(div.novelAUpi$CHROM) # number of windows
## [1] 2334
length(div.novelAUpi$SNP)/length(div.lownatpi$SNP)
## [1] 0.2499732

38.1% of low native diversity SNPs are higher in AU diversity.

summary(div.lownatpi$PI_US)
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 4.667e-06 1.541e-03 2.632e-03 2.357e-03 3.334e-03 4.800e-03
div.novelUSpi <- div.lownatpi[which(div.lownatpi$PI_US > quantile(div.lownatpi$PI_US,0.75)),]
length(div.novelUSpi$SNP)/length(div.lownatpi$SNP)
## [1] 0.2499732

37.7% of low native diversity SNPs are higher in US diversity.

unique(div.novelUSpi$CHROM)
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00  0.00
length(div.novelUSpi$CHROM)
## [1] 2334
intersect(div.novelAUpi$CHROM,div.novelUSpi$CHROM)
##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00  0.00
length(intersect(div.novelAUpi$CHROM,div.novelUSpi$CHROM))
## [1] 31
intersect(div.novelAUpi$SNP,div.novelUSpi$SNP)
##    [1]    32    39    43    44    46    55   123   131   135   150   162   165
##   [13]   166   182   194   195   197   212   218   219   232   233   234   235
##   [25]   242   253   254   255   256   261   278   279   286   287   288   290
##   [37]   295   296   302   303   308   311   313   315   321   322   338   339
##   [49]   341   342   344   360   365   370   371   372   373   376   378   380
##   [61]   383   391   394   399   401   402   405   409   410   412   425   434
##   [73]   436   439   443   445   457   458   460   504   512   519   521   522
##   [85]   526   527   531   534   537   538   539   552   553   554   558   563
##   [97]   573   587   610   632   634   636   648   657   668   677   680   681
##  [109]   683   689   709   710   715   729   730   734   735   736   745   761
##  [121]   762   765   775   776   790   796   823   868   871   901   907   912
##  [133]   913   928   954   956   957   980   983   997  1005  1007  1011  1014
##  [145]  1015  1018  1031  1033  1040  1049  1050  1060  1062  1075  1092  1093
##  [157]  1094  1110  1140  1141  1155  1160  1199  1201  1207  1208  1209  1211
##  [169]  1218  1222  1223  1227  1232  1234  1240  1241  1287  1288  1299  1304
##  [181]  1320  1329  1331  1354  1355  1359  1373  1374  1382  1482  1483  1485
##  [193]  1488  1489  1490  1495  1500  1582  1624  1625  1633  1634  1641  1642
##  [205]  1644  1645  1647  1695  1722  1756  1759  1768  1773  1776  1780  1817
##  [217]  1822  1830  1835  1836  1842  1843  1847  1850  1852  1860  1861  1866
##  [229]  1867  1869  1870  1877  1879  1887  1890  1901  1906  1908  1913  1924
##  [241]  1932  1940  1942  1949  1951  1954  1959  1964  1966  1969  1973  1982
##  [253]  1985  1986  1994  2013  2023  2026  2032  2043  2044  2045  2046  2049
##  [265]  2052  2059  2061  2063  2083  2096  2097  2100  2106  2108  2136  2139
##  [277]  2143  2153  2154  2155  2159  2163  2170  2179  2213  2215  2216  2219
##  [289]  2222  2224  2225  2227  2228  2229  2230  2232  2233  2273  2274  2279
##  [301]  2283  2284  2286  2298  2300  2301  2304  2305  2309  2311  2314  2315
##  [313]  2340  2364  2366  2367  2368  2372  2375  2384  2391  2392  2394  2397
##  [325]  2399  2404  2409  2412  2414  2416  2426  2453  2471  2487  2491  2493
##  [337]  2495  2502  2508  2509  2512  2540  2548  2552  2555  2574  2575  2577
##  [349]  2578  2584  2589  2590  2591  2593  2596  2602  2608  2616  2657  2659
##  [361]  2694  2718  2719  2722  2723  2725  2726  2770  2780  2782  2786  2788
##  [373]  2794  2796  2801  2802  2803  2804  2807  2810  2815  2821  2826  2832
##  [385]  2833  2835  2859  2861  2875  2876  2880  2896  2928  2929  2933  2935
##  [397]  2938  2939  2940  2941  2946  2977  2981  3006  3008  3011  3048  3049
##  [409]  3052  3053  3061  3066  3072  3080  3090  3091  3093  3099  3101  3102
##  [421]  3104  3129  3135  3136  3138  3155  3166  3171  3172  3176  3177  3201
##  [433]  3215  3216  3220  3236  3248  3251  3252  3313  3370  3372  3469  3476
##  [445]  3502  3506  3518  3524  3528  3534  3536  3539  3540  3541  3544  3556
##  [457]  3562  3563  3611  3615  3635  3637  3638  3642  3657  3670  3679  3684
##  [469]  3695  3696  3697  3698  3700  3701  3703  3705  3721  3724  3731  3741
##  [481]  3745  3751  3755  3892  3903  3907  3909  3910  3919  3921  3955  3957
##  [493]  3962  3986  3992  3993  4015  4063  4064  4088  4108  4115  4122  4132
##  [505]  4135  4137  4145  4149  4164  4166  4169  4207  4229  4240  4241  4242
##  [517]  4249  4250  4257  4265  4266  4267  4273  4279  4280  4289  4306  4324
##  [529]  4326  4331  4345  4346  4349  4370  4384  4386  4445  4446  4447  4460
##  [541]  4461  4462  4467  4472  4473  4478  4490  4491  4496  4499  4501  4526
##  [553]  4527  4528  4531  4535  4536  4555  4558  4563  4574  4579  4582  4583
##  [565]  4605  4621  4638  4644  4653  4656  4657  4658  4659  4660  4661  4662
##  [577]  4671  4672  4675  4683  4711  4712  4713  4731  4734  4752  4760  4772
##  [589]  4781  4786  4800  4805  4806  4812  4814  4816  4817  4820  4829  4837
##  [601]  4843  4858  4859  4870  4886  4896  4912  4913  4914  4933  4934  4935
##  [613]  4939  4944  4948  4951  4963  4964  4966  4987  4988  4989  4992  4993
##  [625]  4999  5012  5014  5025  5043  5045  5048  5051  5056  5057  5070  5076
##  [637]  5106  5112  5164  5168  5180  5189  5191  5192  5193  5198  5199  5201
##  [649]  5205  5207  5210  5214  5215  5216  5239  5243  5253  5341  5347  5370
##  [661]  5456  5483  5489  5493  5494  5510  5515  5519  5528  5544  5558  5568
##  [673]  5591  5596  5617  5624  5630  5631  5639  5658  5660  5669  5678  5681
##  [685]  5686  5690  5711  5713  5735  5740  5741  5773  5774  5795  5800  5808
##  [697]  5813  5837  5846  5847  5853  5865  5873  5875  5877  5931  5942  5945
##  [709]  5958  5961  5977  5987  5999  6027  6045  6047  6048  6063  6066  6067
##  [721]  6078  6081  6082  6085  6086  6091  6094  6096  6100  6106  6143  6202
##  [733]  6203  6239  6347  6364  6384  6386  6390  6399  6417  6419  6597  6598
##  [745]  6599  6602  6653  6692  6694  6700  6701  6706  6716  6717  6727  6729
##  [757]  6737  6738  6741  6742  6743  6744  6748  6758  6759  6763  6770  6771
##  [769]  6780  6787  6802  6804  6805  6829  6838  6842  6869  6884  6885  6896
##  [781]  6907  6908  6909  6912  6941  6942  6977  6979  6986  6991  6995  6996
##  [793]  7003  7005  7010  7021  7023  7024  7031  7032  7049  7054  7098  7138
##  [805]  7139  7140  7141  7144  7158  7165  7215  7226  7243  7245  7273  7275
##  [817]  7276  7280  7283  7285  7286  7291  7295  7306  7307  7318  7324  7329
##  [829]  7346  7357  7360  7365  7372  7378  7381  7382  7384  7387  7388  7390
##  [841]  7391  7393  7414  7415  7416  7417  7420  7424  7426  7428  7441  7447
##  [853]  7451  7453  7456  7457  7458  7460  7462  7467  7476  7490  7491  7532
##  [865]  7535  7540  7545  7547  7552  7553  7563  7566  7568  7570  7573  7575
##  [877]  7599  7600  7602  7604  7605  7610  7616  7618  7619  7621  7635  7649
##  [889]  7650  7653  7668  7687  7688  7692  7693  7697  7700  7704  7719  7725
##  [901]  7760  7773  7782  7784  7785  7794  7796  7803  7804  7807  7816  7848
##  [913]  7849  7852  7856  7860  7878  7883  7884  7890  7892  7993  8000  8033
##  [925]  8042  8045  8047  8068  8069  8072  8082  8114  8117  8127  8132  8133
##  [937]  8142  8143  8146  8149  8151  8153  8156  8159  8160  8164  8165  8170
##  [949]  8188  8191  8200  8201  8202  8226  8228  8243  8275  8303  8305  8317
##  [961]  8340  8366  8380  8381  8385  8386  8398  8399  8400  8402  8403  8408
##  [973]  8411  8431  8446  8447  8449  8452  8461  8480  8499  8500  8504  8520
##  [985]  8523  8569  8570  8584  8587  8593  8629  8630  8632  8638  8640  8644
##  [997]  8662  8667  8669  8702  8704  8706  8714  8868  8879  8882  8889  8897
## [1009]  8899  8900  8902  8905  8910  8913  8920  8934  9015  9023  9075  9078
## [1021]  9081  9086  9100  9102  9104  9135  9140  9154  9195  9197  9275  9278
## [1033]  9298  9312  9325  9331  9340  9345  9346  9369  9377  9381  9386  9389
## [1045]  9390  9391  9392  9415  9418  9421  9457  9460  9463  9470  9471  9472
## [1057]  9485  9486  9511  9513  9523  9541  9551  9554  9556  9557  9574  9580
## [1069]  9584  9585  9596  9597  9630  9631  9635  9648  9651  9653  9664  9675
## [1081]  9677  9680  9686  9688  9689  9697  9714  9718  9719  9726  9731  9752
## [1093]  9776  9779  9783  9791  9792  9797  9800  9805  9806  9807  9810  9811
## [1105]  9818  9819  9820  9841  9842  9853  9855  9873  9911  9934  9947  9971
## [1117]  9984  9988  9994  9995 10005 10014 10021 10026 10043 10044 10054 10055
## [1129] 10072 10091 10126 10127 10138 10140 10145 10146 10162 10186 10191 10196
## [1141] 10217 10228 10279 10281 10291 10300 10302 10313 10315 10316 10329 10361
## [1153] 10380 10386 10404 10412 10414 10421 10423 10482 10483 10489 10504 10519
## [1165] 10532 10533 10536 10553 10577 10582 10601 10604 10610 10639 10653 10657
## [1177] 10678 10705 10711 10721 10744 10746 10747 10776 10811 10812 10825 10854
## [1189] 10882 10952 10963 11011 11024 11025 11031 11044 11045 11068 11073 11121
## [1201] 11122 11130 11131 11132 11140 11143 11211 11238 11283 11287 11288 11313
## [1213] 11324 11325 11334 11335 11337 11371 11387 11388 11397 11405 11412 11417
## [1225] 11419 11438 11445 11449 11483 11488 11505 11509 11511 11512 11514 11515
## [1237] 11519 11528 11558 11601 11631 11633 11650 11658 11663 11670 11674 11691
## [1249] 11692 11708 11710 11726 11733 11775 11781 11783 11814 11815 11848 11856
## [1261] 11865 11870 11872 11898 11922 11926 11928 11929 11936 11937 11938 11941
## [1273] 11943 11944 11955 11962 11966 11970 11971 11976 11978 11985 12016 12017
## [1285] 12020 12034 12055 12060 12064 12070 12071 12074 12085 12088 12089 12124
## [1297] 12138 12166 12170 12197 12219 12220 12222 12236 12238 12285 12287 12288
## [1309] 12296 12320 12322 12323 12325 12362 12366 12367 12378 12380 12391 12410
## [1321] 12411 12417 12428 12429 12432 12438 12442 12447 12450 12458 12468 12469
## [1333] 12472 12473 12482 12489 12499 12504 12518 12520 12526 12527 12552 12560
## [1345] 12564 12565 12593 12594 12629 12636 12668 12679 12680 12694 12706 12721
## [1357] 12736 12738 12740 12765 12772 12774 12776 12785 12795 12797 12802 12807
## [1369] 12820 12821 12822 12823 12825 12829 12831 12860 12861 12870 12872 12878
## [1381] 12897 12915 12916 12924 12925 12927 12937 12941 12943 12946 12948 12955
## [1393] 12961 12962 12974 12976 12979 12982 12983 12986 13013 13014 13016 13025
## [1405] 13036 13085 13103 13105 13106 13107 13113 13118 13119 13120 13138 13140
## [1417] 13145 13146 13148 13157 13158 13159 13160 13171 13173 13176 13178 13276
## [1429] 13277 13278 13285 13287 13289 13290 13291 13297 13304 13318 13332 13339
## [1441] 13342 13343 13346 13347 13359 13363 13370 13410 13444 13456 13468 13502
## [1453] 13592 13595 13632 13688 13695 13733 13734 13766 13812 13814 13862 13864
## [1465] 13875 13877 14110 14115 14119 14120 14122 14127 14133 14140 14146 14148
## [1477] 14160 14168 14171 14177 14193 14208 14222 14227 14231 14232 14254 14261
## [1489] 14262 14268 14277 14313 14320 14321 14345 14354 14388 14390 14398 14410
## [1501] 14415 14422 14423 14434 14444 14445 14455 14471 14477 14493 14506 14508
## [1513] 14538 14545 14552 14553 14554 14558 14582 14585 14587 14616 14626 14627
## [1525] 14633 14636 14713 14725 14727 14732 14742 14775 14782 14814 14839 14841
## [1537] 14842 14843 14851 14874 14875 14878 14881 14882 14892 14893 14896 14903
## [1549] 14909 14916 14918 14947 14997 15017 15023 15061 15103 15174 15189 15193
## [1561] 15213 15214 15220 15221 15226 15232 15234 15235 15236 15261 15285 15370
## [1573] 15386 15390 15394 15396 15420 15448 15490 15494 15495 15496 15506 15545
## [1585] 15549 15554 15556 15560 15592 15596 15598 15638 15641 15646 15660 15668
## [1597] 15672 15673 15682 15694 15714 15715 15717 15720 15722 15723 15728 15731
## [1609] 15740 15746 15752 15760 15761 15763 15770 15771 15788 15795 15803 15805
## [1621] 15806 15807 15814 15825 15834 15840 15842 15861 15882 15886 15891 15892
## [1633] 15896 15903 15905 15908 15910 15911 15913 15914 15967 15969 15976 15981
## [1645] 15983 15985 15997 16023 16036 16072 16087 16090 16095 16113 16130 16134
## [1657] 16138 16143 16207 16208 16214 16219 16220 16222 16226 16238 16289 16319
## [1669] 16323 16329 16374 16375 16376 16391 16399 16439 16444 16447 16449 16454
## [1681] 16456 16459 16466 16468 16470 16503 16509 16519 16522 16526 16527 16529
## [1693] 16530 16538 16542 16544 16551 16552 16554 16557 16561 16565 16611 16622
## [1705] 16625 16626 16630 16631 16633 16640 16668 16669 16670 16674 16681 16692
## [1717] 16698 16701 16709 16746 16757 16781 16789 16790 16792 16795 16796 16800
## [1729] 16808 16809 16857 16858 16862 16863 16877 16882 16883 16889 16907 16914
## [1741] 16915 16919 16931 16938 16939 16943 16945 16946 16951 16960 16976 16978
## [1753] 16984 16987 16990 17000 17022 17026 17029 17032 17079 17080 17084 17085
## [1765] 17088 17099 17100 17102 17104 17112 17113 17119 17125 17127 17128 17135
## [1777] 17142 17143 17185 17198 17204 17207 17210 17238 17244 17247 17250 17260
## [1789] 17262 17277 17278 17297 17299 17324 17329 17347 17364 17403 17407 17411
## [1801] 17420 17445 17475 17478 17551 17559 17576 17590 17628 17648 17651 17652
## [1813] 17654 17726 17731 17736 17737 17738 17746 17750 17766 17785 17788 17800
## [1825] 17803 17809 17826 17828 17832 17836 17837 17840 17841 17849 17854 17855
## [1837] 17859 17869 17924 17925 17935 17977 18012 18014 18018 18028 18031 18040
## [1849] 18042 18054 18070 18075 18081 18100 18108 18121 18123 18124 18138 18141
## [1861] 18143 18144 18148 18152 18162 18166 18171 18178 18187 18188 18193 18216
## [1873] 18217 18252 18254 18266 18268 18273 18275 18345 18358 18361 18363 18368
## [1885] 18387 18406 18416 18417 18444 18451 18452 18453 18460 18462 18475 18480
## [1897] 18506 18527 18550 18558 18561 18582 18617 18635 18636 18638 18640 18643
## [1909] 18646 18650 18654 18659 18663 18667 18671 18674 18675 18676 18707 18708
## [1921] 18726 18733 18783 18784 18808 18811 18815 18827 18830 18834 18886 18887
## [1933] 18893 18903 18904 18943 19006 19021 19022 19028 19034 19035 19042 19045
## [1945] 19048 19065 19066 19067 19075 19076 19109 19114 19115 19118 19120 19137
## [1957] 19144 19164 19165 19169 19308 19318 19326 19328 19381 19388 19392 19396
## [1969] 19397 19427 19441 19442 19455 19456 19460 19464 19486 19525 19530 19538
## [1981] 19541 19542 19575 19576 19577 19578 19580 19585 19595 19599 19624 19643
## [1993] 19644 19645 19658 19659 19660 19662 19664 19668 19673 19675 19677 19681
## [2005] 19694 19703 19758 19759 19761 19780 19808 19821 19824 19825 19856 19863
## [2017] 19902 19974 20016 20019 20038 20039 20055 20056 20065
length(intersect(div.novelAUpi$SNP,div.novelUSpi$SNP))
## [1] 2025

What’s going on at outlier windows in particular?

div.outliers.hiAUpi <- div.outliers.AUUK[which(div.outliers.AUUK$PI_AU > div.outliers.AUUK$PI_UK),]
unique(div.outliers.hiAUpi$CHROM)
##  [1] 12.00 13.00  1.25  1.00 23.00 27.00  2.00  3.00  4.50  5.00  6.00  0.00
length(div.outliers.hiAUpi$SNP)
## [1] 91
length(div.outliers.AUUK$SNP)
## [1] 201
length(div.outliers.hiAUpi$SNP)/length(div.outliers.AUUK$SNP)
## [1] 0.4527363

85% of FST outlier windows have higher diversity in AU

div.outliers.hiUSpi <- div.outliers.USUK[which(div.outliers.USUK$PI_US > div.outliers.USUK$PI_UK),]
unique(div.outliers.hiUSpi$CHROM) 
## [1] 12.00 17.00  1.25  1.00  2.00  3.00  4.00  6.00  0.00
length(div.outliers.hiUSpi$SNP) 
## [1] 61
length(div.outliers.USUK$SNP)
## [1] 201
length(div.outliers.hiUSpi$SNP)/length(div.outliers.USUK$SNP)
## [1] 0.3034826

Only 38% of FST outlier windows have higher diversity in US

intersect(div.outliers.hiUSpi$CHROM,div.outliers.hiAUpi$CHROM)
## [1] 12.00  1.25  1.00  2.00  3.00  6.00  0.00

None of these regions overlap between invasions.

Diversity underlying candidate peaks

Chromosome 2

chrom2.div <- div[which(div$CHROM==2),]
unique(chrom2.div$SNP)
##    [1]  7875  7876  7877  7878  7879  7880  7881  7882  7883  7884  7885  7886
##   [13]  7887  7888  7889  7890  7891  7892  7893  7894  7895  7896  7897  7898
##   [25]  7899  7900  7901  7902  7903  7904  7905  7906  7907  7908  7909  7910
##   [37]  7911  7912  7913  7914  7915  7916  7917  7918  7919  7920  7921  7922
##   [49]  7923  7924  7925  7926  7927  7928  7929  7930  7931  7932  7933  7934
##   [61]  7935  7936  7937  7938  7939  7940  7941  7942  7943  7944  7945  7946
##   [73]  7947  7948  7949  7950  7951  7952  7953  7954  7955  7956  7957  7958
##   [85]  7959  7960  7961  7962  7963  7964  7965  7966  7967  7968  7969  7970
##   [97]  7971  7972  7973  7974  7975  7976  7977  7978  7979  7980  7981  7982
##  [109]  7983  7984  7985  7986  7987  7988  7989  7990  7991  7992  7993  7994
##  [121]  7995  7996  7997  7998  7999  8000  8001  8002  8003  8004  8005  8006
##  [133]  8007  8008  8009  8010  8011  8012  8013  8014  8015  8016  8017  8018
##  [145]  8019  8020  8021  8022  8023  8024  8025  8026  8027  8028  8029  8030
##  [157]  8031  8032  8033  8034  8035  8036  8037  8038  8039  8040  8041  8042
##  [169]  8043  8044  8045  8046  8047  8048  8049  8050  8051  8052  8053  8054
##  [181]  8055  8056  8057  8058  8059  8060  8061  8062  8063  8064  8065  8066
##  [193]  8067  8068  8069  8070  8071  8072  8073  8074  8075  8076  8077  8078
##  [205]  8079  8080  8081  8082  8083  8084  8085  8086  8087  8088  8089  8090
##  [217]  8091  8092  8093  8094  8095  8096  8097  8098  8099  8100  8101  8102
##  [229]  8103  8104  8105  8106  8107  8108  8109  8110  8111  8112  8113  8114
##  [241]  8115  8116  8117  8118  8119  8120  8121  8122  8123  8124  8125  8126
##  [253]  8127  8128  8129  8130  8131  8132  8133  8134  8135  8136  8137  8138
##  [265]  8139  8140  8141  8142  8143  8144  8145  8146  8147  8148  8149  8150
##  [277]  8151  8152  8153  8154  8155  8156  8157  8158  8159  8160  8161  8162
##  [289]  8163  8164  8165  8166  8167  8168  8169  8170  8171  8172  8173  8174
##  [301]  8175  8176  8177  8178  8179  8180  8181  8182  8183  8184  8185  8186
##  [313]  8187  8188  8189  8190  8191  8192  8193  8194  8195  8196  8197  8198
##  [325]  8199  8200  8201  8202  8203  8204  8205  8206  8207  8208  8209  8210
##  [337]  8211  8212  8213  8214  8215  8216  8217  8218  8219  8220  8221  8222
##  [349]  8223  8224  8225  8226  8227  8228  8229  8230  8231  8232  8233  8234
##  [361]  8235  8236  8237  8238  8239  8240  8241  8242  8243  8244  8245  8246
##  [373]  8247  8248  8249  8250  8251  8252  8253  8254  8255  8256  8257  8258
##  [385]  8259  8260  8261  8262  8263  8264  8265  8266  8267  8268  8269  8270
##  [397]  8271  8272  8273  8274  8275  8276  8277  8278  8279  8280  8281  8282
##  [409]  8283  8284  8285  8286  8287  8288  8289  8290  8291  8292  8293  8294
##  [421]  8295  8296  8297  8298  8299  8300  8301  8302  8303  8304  8305  8306
##  [433]  8307  8308  8309  8310  8311  8312  8313  8314  8315  8316  8317  8318
##  [445]  8319  8320  8321  8322  8323  8324  8325  8326  8327  8328  8329  8330
##  [457]  8331  8332  8333  8334  8335  8336  8337  8338  8339  8340  8341  8342
##  [469]  8343  8344  8345  8346  8347  8348  8349  8350  8351  8352  8353  8354
##  [481]  8355  8356  8357  8358  8359  8360  8361  8362  8363  8364  8365  8366
##  [493]  8367  8368  8369  8370  8371  8372  8373  8374  8375  8376  8377  8378
##  [505]  8379  8380  8381  8382  8383  8384  8385  8386  8387  8388  8389  8390
##  [517]  8391  8392  8393  8394  8395  8396  8397  8398  8399  8400  8401  8402
##  [529]  8403  8404  8405  8406  8407  8408  8409  8410  8411  8412  8413  8414
##  [541]  8415  8416  8417  8418  8419  8420  8421  8422  8423  8424  8425  8426
##  [553]  8427  8428  8429  8430  8431  8432  8433  8434  8435  8436  8437  8438
##  [565]  8439  8440  8441  8442  8443  8444  8445  8446  8447  8448  8449  8450
##  [577]  8451  8452  8453  8454  8455  8456  8457  8458  8459  8460  8461  8462
##  [589]  8463  8464  8465  8466  8467  8468  8469  8470  8471  8472  8473  8474
##  [601]  8475  8476  8477  8478  8479  8480  8481  8482  8483  8484  8485  8486
##  [613]  8487  8488  8489  8490  8491  8492  8493  8494  8495  8496  8497  8498
##  [625]  8499  8500  8501  8502  8503  8504  8505  8506  8507  8508  8509  8510
##  [637]  8511  8512  8513  8514  8515  8516  8517  8518  8519  8520  8521  8522
##  [649]  8523  8524  8525  8526  8527  8528  8529  8530  8531  8532  8533  8534
##  [661]  8535  8536  8537  8538  8539  8540  8541  8542  8543  8544  8545  8546
##  [673]  8547  8548  8549  8550  8551  8552  8553  8554  8555  8556  8557  8558
##  [685]  8559  8560  8561  8562  8563  8564  8565  8566  8567  8568  8569  8570
##  [697]  8571  8572  8573  8574  8575  8576  8577  8578  8579  8580  8581  8582
##  [709]  8583  8584  8585  8586  8587  8588  8589  8590  8591  8592  8593  8594
##  [721]  8595  8596  8597  8598  8599  8600  8601  8602  8603  8604  8605  8606
##  [733]  8607  8608  8609  8610  8611  8612  8613  8614  8615  8616  8617  8618
##  [745]  8619  8620  8621  8622  8623  8624  8625  8626  8627  8628  8629  8630
##  [757]  8631  8632  8633  8634  8635  8636  8637  8638  8639  8640  8641  8642
##  [769]  8643  8644  8645  8646  8647  8648  8649  8650  8651  8652  8653  8654
##  [781]  8655  8656  8657  8658  8659  8660  8661  8662  8663  8664  8665  8666
##  [793]  8667  8668  8669  8670  8671  8672  8673  8674  8675  8676  8677  8678
##  [805]  8679  8680  8681  8682  8683  8684  8685  8686  8687  8688  8689  8690
##  [817]  8691  8692  8693  8694  8695  8696  8697  8698  8699  8700  8701  8702
##  [829]  8703  8704  8705  8706  8707  8708  8709  8710  8711  8712  8713  8714
##  [841]  8715  8716  8717  8718  8719  8720  8721  8722  8723  8724  8725  8726
##  [853]  8727  8728  8729  8730  8731  8732  8733  8734  8735  8736  8737  8738
##  [865]  8739  8740  8741  8742  8743  8744  8745  8746  8747  8748  8749  8750
##  [877]  8751  8752  8753  8754  8755  8756  8757  8758  8759  8760  8761  8762
##  [889]  8763  8764  8765  8766  8767  8768  8769  8770  8771  8772  8773  8774
##  [901]  8775  8776  8777  8778  8779  8780  8781  8782  8783  8784  8785  8786
##  [913]  8787  8788  8789  8790  8791  8792  8793  8794  8795  8796  8797  8798
##  [925]  8799  8800  8801  8802  8803  8804  8805  8806  8807  8808  8809  8810
##  [937]  8811  8812  8813  8814  8815  8816  8817  8818  8819  8820  8821  8822
##  [949]  8823  8824  8825  8826  8827  8828  8829  8830  8831  8832  8833  8834
##  [961]  8835  8836  8837  8838  8839  8840  8841  8842  8843  8844  8845  8846
##  [973]  8847  8848  8849  8850  8851  8852  8853  8854  8855  8856  8857  8858
##  [985]  8859  8860  8861  8862  8863  8864  8865  8866  8867  8868  8869  8870
##  [997]  8871  8872  8873  8874  8875  8876  8877  8878  8879  8880  8881  8882
## [1009]  8883  8884  8885  8886  8887  8888  8889  8890  8891  8892  8893  8894
## [1021]  8895  8896  8897  8898  8899  8900  8901  8902  8903  8904  8905  8906
## [1033]  8907  8908  8909  8910  8911  8912  8913  8914  8915  8916  8917  8918
## [1045]  8919  8920  8921  8922  8923  8924  8925  8926  8927  8928  8929  8930
## [1057]  8931  8932  8933  8934  8935  8936  8937  8938  8939  8940  8941  8942
## [1069]  8943  8944  8945  8946  8947  8948  8949  8950  8951  8952  8953  8954
## [1081]  8955  8956  8957  8958  8959  8960  8961  8962  8963  8964  8965  8966
## [1093]  8967  8968  8969  8970  8971  8972  8973  8974  8975  8976  8977  8978
## [1105]  8979  8980  8981  8982  8983  8984  8985  8986  8987  8988  8989  8990
## [1117]  8991  8992  8993  8994  8995  8996  8997  8998  8999  9000  9001  9002
## [1129]  9003  9004  9005  9006  9007  9008  9009  9010  9011  9012  9013  9014
## [1141]  9015  9016  9017  9018  9019  9020  9021  9022  9023  9024  9025  9026
## [1153]  9027  9028  9029  9030  9031  9032  9033  9034  9035  9036  9037  9038
## [1165]  9039  9040  9041  9042  9043  9044  9045  9046  9047  9048  9049  9050
## [1177]  9051  9052  9053  9054  9055  9056  9057  9058  9059  9060  9061  9062
## [1189]  9063  9064  9065  9066  9067  9068  9069  9070  9071  9072  9073  9074
## [1201]  9075  9076  9077  9078  9079  9080  9081  9082  9083  9084  9085  9086
## [1213]  9087  9088  9089  9090  9091  9092  9093  9094  9095  9096  9097  9098
## [1225]  9099  9100  9101  9102  9103  9104  9105  9106  9107  9108  9109  9110
## [1237]  9111  9112  9113  9114  9115  9116  9117  9118  9119  9120  9121  9122
## [1249]  9123  9124  9125  9126  9127  9128  9129  9130  9131  9132  9133  9134
## [1261]  9135  9136  9137  9138  9139  9140  9141  9142  9143  9144  9145  9146
## [1273]  9147  9148  9149  9150  9151  9152  9153  9154  9155  9156  9157  9158
## [1285]  9159  9160  9161  9162  9163  9164  9165  9166  9167  9168  9169  9170
## [1297]  9171  9172  9173  9174  9175  9176  9177  9178  9179  9180  9181  9182
## [1309]  9183  9184  9185  9186  9187  9188  9189  9190  9191  9192  9193  9194
## [1321]  9195  9196  9197  9198  9199  9200  9201  9202  9203  9204  9205  9206
## [1333]  9207  9208  9209  9210  9211  9212  9213  9214  9215  9216  9217  9218
## [1345]  9219  9220  9221  9222  9223  9224  9225  9226  9227  9228  9229  9230
## [1357]  9231  9232  9233  9234  9235  9236  9237  9238  9239  9240  9241  9242
## [1369]  9243  9244  9245  9246  9247  9248  9249  9250  9251  9252  9253  9254
## [1381]  9255  9256  9257  9258  9259  9260  9261  9262  9263  9264  9265  9266
## [1393]  9267  9268  9269  9270  9271  9272  9273  9274  9275  9276  9277  9278
## [1405]  9279  9280  9281  9282  9283  9284  9285  9286  9287  9288  9289  9290
## [1417]  9291  9292  9293  9294  9295  9296  9297  9298  9299  9300  9301  9302
## [1429]  9303  9304  9305  9306  9307  9308  9309  9310  9311  9312  9313  9314
## [1441]  9315  9316  9317  9318  9319  9320  9321  9322  9323  9324  9325  9326
## [1453]  9327  9328  9329  9330  9331  9332  9333  9334  9335  9336  9337  9338
## [1465]  9339  9340  9341  9342  9343  9344  9345  9346  9347  9348  9349  9350
## [1477]  9351  9352  9353  9354  9355  9356  9357  9358  9359  9360  9361  9362
## [1489]  9363  9364  9365  9366  9367  9368  9369  9370  9371  9372  9373  9374
## [1501]  9375  9376  9377  9378  9379  9380  9381  9382  9383  9384  9385  9386
## [1513]  9387  9388  9389  9390  9391  9392  9393  9394  9395  9396  9397  9398
## [1525]  9399  9400  9401  9402  9403  9404  9405  9406  9407  9408  9409  9410
## [1537]  9411  9412  9413  9414  9415  9416  9417  9418  9419  9420  9421  9422
## [1549]  9423  9424  9425  9426  9427  9428  9429  9430  9431  9432  9433  9434
## [1561]  9435  9436  9437  9438  9439  9440  9441  9442  9443  9444  9445  9446
## [1573]  9447  9448  9449  9450  9451  9452  9453  9454  9455  9456  9457  9458
## [1585]  9459  9460  9461  9462  9463  9464  9465  9466  9467  9468  9469  9470
## [1597]  9471  9472  9473  9474  9475  9476  9477  9478  9479  9480  9481  9482
## [1609]  9483  9484  9485  9486  9487  9488  9489  9490  9491  9492  9493  9494
## [1621]  9495  9496  9497  9498  9499  9500  9501  9502  9503  9504  9505  9506
## [1633]  9507  9508  9509  9510  9511  9512  9513  9514  9515  9516  9517  9518
## [1645]  9519  9520  9521  9522  9523  9524  9525  9526  9527  9528  9529  9530
## [1657]  9531  9532  9533  9534  9535  9536  9537  9538  9539  9540  9541  9542
## [1669]  9543  9544  9545  9546  9547  9548  9549  9550  9551  9552  9553  9554
## [1681]  9555  9556  9557  9558  9559  9560  9561  9562  9563  9564  9565  9566
## [1693]  9567  9568  9569  9570  9571  9572  9573  9574  9575  9576  9577  9578
## [1705]  9579  9580  9581  9582  9583  9584  9585  9586  9587  9588  9589  9590
## [1717]  9591  9592  9593  9594  9595  9596  9597  9598  9599  9600  9601  9602
## [1729]  9603  9604  9605  9606  9607  9608  9609  9610  9611  9612  9613  9614
## [1741]  9615  9616  9617  9618  9619  9620  9621  9622  9623  9624  9625  9626
## [1753]  9627  9628  9629  9630  9631  9632  9633  9634  9635  9636  9637  9638
## [1765]  9639  9640  9641  9642  9643  9644  9645  9646  9647  9648  9649  9650
## [1777]  9651  9652  9653  9654  9655  9656  9657  9658  9659  9660  9661  9662
## [1789]  9663  9664  9665  9666  9667  9668  9669  9670  9671  9672  9673  9674
## [1801]  9675  9676  9677  9678  9679  9680  9681  9682  9683  9684  9685  9686
## [1813]  9687  9688  9689  9690  9691  9692  9693  9694  9695  9696  9697  9698
## [1825]  9699  9700  9701  9702  9703  9704  9705  9706  9707  9708  9709  9710
## [1837]  9711  9712  9713  9714  9715  9716  9717  9718  9719  9720  9721  9722
## [1849]  9723  9724  9725  9726  9727  9728  9729  9730  9731  9732  9733  9734
## [1861]  9735  9736  9737  9738  9739  9740  9741  9742  9743  9744  9745  9746
## [1873]  9747  9748  9749  9750  9751  9752  9753  9754  9755  9756  9757  9758
## [1885]  9759  9760  9761  9762  9763  9764  9765  9766  9767  9768  9769  9770
## [1897]  9771  9772  9773  9774  9775  9776  9777  9778  9779  9780  9781  9782
## [1909]  9783  9784  9785  9786  9787  9788  9789  9790  9791  9792  9793  9794
## [1921]  9795  9796  9797  9798  9799  9800  9801  9802  9803  9804  9805  9806
## [1933]  9807  9808  9809  9810  9811  9812  9813  9814  9815  9816  9817  9818
## [1945]  9819  9820  9821  9822  9823  9824  9825  9826  9827  9828  9829  9830
## [1957]  9831  9832  9833  9834  9835  9836  9837  9838  9839  9840  9841  9842
## [1969]  9843  9844  9845  9846  9847  9848  9849  9850  9851  9852  9853  9854
## [1981]  9855  9856  9857  9858  9859  9860  9861  9862  9863  9864  9865  9866
## [1993]  9867  9868  9869  9870  9871  9872  9873  9874  9875  9876  9877  9878
## [2005]  9879  9880  9881  9882  9883  9884  9885  9886  9887  9888  9889  9890
## [2017]  9891  9892  9893  9894  9895  9896  9897  9898  9899  9900  9901  9902
## [2029]  9903  9904  9905  9906  9907  9908  9909  9910  9911  9912  9913  9914
## [2041]  9915  9916  9917  9918  9919  9920  9921  9922  9923  9924  9925  9926
## [2053]  9927  9928  9929  9930  9931  9932  9933  9934  9935  9936  9937  9938
## [2065]  9939  9940  9941  9942  9943  9944  9945  9946  9947  9948  9949  9950
## [2077]  9951  9952  9953  9954  9955  9956  9957  9958  9959  9960  9961  9962
## [2089]  9963  9964  9965  9966  9967  9968  9969  9970  9971  9972  9973  9974
## [2101]  9975  9976  9977  9978  9979  9980  9981  9982  9983  9984  9985  9986
## [2113]  9987  9988  9989  9990  9991  9992  9993  9994  9995  9996  9997  9998
## [2125]  9999 10000 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010
## [2137] 10011 10012 10013 10014 10015 10016 10017 10018 10019 10020 10021 10022
## [2149] 10023 10024 10025 10026 10027 10028 10029 10030 10031 10032 10033 10034
## [2161] 10035 10036 10037 10038 10039 10040 10041 10042 10043 10044 10045 10046
## [2173] 10047 10048 10049 10050 10051 10052 10053 10054 10055 10056 10057 10058
## [2185] 10059 10060 10061 10062 10063 10064 10065 10066 10067 10068 10069 10070
## [2197] 10071 10072 10073 10074 10075 10076 10077 10078 10079 10080 10081 10082
## [2209] 10083 10084 10085 10086 10087 10088 10089 10090 10091 10092 10093 10094
## [2221] 10095 10096 10097 10098 10099 10100 10101 10102 10103 10104 10105 10106
## [2233] 10107 10108 10109 10110 10111 10112 10113 10114 10115 10116 10117 10118
## [2245] 10119 10120 10121 10122 10123 10124 10125 10126 10127 10128 10129 10130
## [2257] 10131 10132 10133 10134 10135 10136 10137 10138 10139 10140 10141 10142
## [2269] 10143 10144 10145 10146 10147 10148 10149 10150 10151 10152 10153 10154
## [2281] 10155 10156 10157 10158 10159 10160 10161 10162 10163 10164 10165 10166
## [2293] 10167 10168 10169 10170 10171 10172 10173 10174 10175 10176 10177 10178
## [2305] 10179 10180 10181 10182 10183 10184 10185 10186 10187 10188 10189 10190
## [2317] 10191 10192 10193 10194 10195 10196 10197 10198 10199 10200 10201 10202
## [2329] 10203 10204 10205 10206 10207 10208 10209 10210 10211 10212 10213 10214
## [2341] 10215 10216 10217 10218 10219 10220 10221 10222 10223 10224 10225 10226
## [2353] 10227 10228 10229 10230 10231 10232 10233 10234 10235 10236 10237 10238
## [2365] 10239 10240 10241 10242 10243 10244 10245 10246 10247 10248 10249 10250
## [2377] 10251 10252 10253 10254 10255 10256 10257 10258 10259 10260 10261 10262
## [2389] 10263 10264 10265 10266 10267 10268 10269 10270 10271 10272 10273 10274
## [2401] 10275 10276 10277 10278 10279 10280 10281 10282 10283 10284 10285 10286
## [2413] 10287 10288 10289 10290 10291 10292 10293 10294 10295 10296 10297 10298
## [2425] 10299 10300 10301 10302 10303 10304 10305 10306 10307 10308 10309 10310
## [2437] 10311 10312 10313 10314 10315 10316 10317 10318 10319 10320 10321 10322
## [2449] 10323 10324 10325 10326 10327 10328 10329 10330 10331 10332 10333 10334
## [2461] 10335 10336 10337 10338 10339 10340 10341 10342 10343 10344 10345 10346
## [2473] 10347 10348 10349 10350 10351 10352 10353 10354 10355 10356 10357 10358
## [2485] 10359 10360 10361 10362 10363 10364 10365 10366 10367 10368 10369 10370
## [2497] 10371 10372 10373 10374 10375 10376 10377 10378 10379 10380 10381 10382
## [2509] 10383 10384 10385 10386 10387 10388 10389 10390 10391 10392 10393 10394
## [2521] 10395 10396 10397 10398 10399 10400 10401 10402 10403 10404 10405 10406
## [2533] 10407 10408 10409 10410 10411 10412 10413 10414 10415 10416 10417 10418
## [2545] 10419 10420 10421 10422 10423 10424 10425 10426 10427 10428 10429 10430
## [2557] 10431 10432 10433 10434 10435 10436 10437 10438 10439 10440 10441 10442
## [2569] 10443 10444 10445 10446 10447 10448 10449 10450 10451 10452 10453 10454
## [2581] 10455 10456 10457 10458 10459 10460 10461 10462 10463 10464 10465 10466
## [2593] 10467 10468 10469 10470 10471 10472 10473 10474 10475 10476 10477 10478
## [2605] 10479 10480 10481 10482 10483 10484 10485 10486 10487 10488 10489 10490
## [2617] 10491 10492 10493 10494 10495 10496 10497 10498 10499 10500 10501 10502
## [2629] 10503 10504 10505 10506 10507 10508 10509 10510 10511 10512 10513 10514
## [2641] 10515 10516 10517 10518 10519 10520 10521 10522 10523 10524 10525 10526
## [2653] 10527 10528 10529 10530 10531 10532 10533 10534 10535 10536 10537 10538
## [2665] 10539 10540 10541 10542 10543 10544 10545 10546 10547 10548 10549 10550
## [2677] 10551 10552 10553 10554 10555 10556 10557 10558 10559 10560 10561 10562
## [2689] 10563 10564 10565 10566 10567 10568 10569 10570 10571 10572 10573 10574
## [2701] 10575 10576 10577 10578 10579 10580 10581 10582 10583 10584 10585 10586
## [2713] 10587 10588 10589 10590 10591 10592 10593 10594 10595 10596 10597 10598
## [2725] 10599 10600 10601 10602 10603 10604 10605 10606 10607 10608 10609 10610
## [2737] 10611 10612 10613 10614 10615 10616 10617 10618 10619 10620 10621 10622
## [2749] 10623 10624 10625 10626 10627 10628 10629 10630 10631 10632 10633 10634
## [2761] 10635 10636 10637 10638 10639 10640 10641 10642 10643 10644 10645 10646
## [2773] 10647 10648 10649 10650 10651 10652 10653 10654 10655 10656 10657 10658
## [2785] 10659 10660 10661 10662 10663 10664 10665 10666 10667 10668 10669 10670
## [2797] 10671 10672 10673 10674 10675 10676 10677 10678 10679 10680 10681 10682
## [2809] 10683 10684 10685 10686 10687 10688 10689 10690 10691 10692 10693 10694
## [2821] 10695 10696 10697 10698 10699 10700 10701 10702 10703 10704 10705 10706
## [2833] 10707 10708 10709 10710 10711 10712 10713 10714 10715 10716 10717 10718
## [2845] 10719 10720 10721 10722 10723 10724 10725 10726 10727 10728 10729 10730
## [2857] 10731 10732 10733 10734 10735 10736 10737 10738 10739 10740 10741 10742
## [2869] 10743 10744 10745 10746 10747 10748 10749 10750 10751 10752 10753 10754
## [2881] 10755 10756 10757 10758 10759 10760 10761 10762 10763 10764 10765 10766
## [2893] 10767 10768 10769 10770 10771 10772 10773 10774 10775 10776 10777 10778
## [2905] 10779 10780 10781 10782 10783 10784 10785 10786 10787 10788 10789 10790
## [2917] 10791 10792 10793 10794 10795 10796 10797 10798 10799 10800 10801 10802
## [2929] 10803 10804 10805 10806 10807 10808 10809 10810 10811 10812 10813 10814
## [2941] 10815 10816 10817 10818 10819 10820 10821 10822 10823 10824 10825 10826
## [2953] 10827 10828 10829 10830 10831 10832 10833 10834 10835 10836 10837 10838
## [2965] 10839 10840 10841 10842 10843 10844 10845 10846 10847 10848 10849 10850
## [2977] 10851 10852 10853 10854 10855 10856 10857 10858 10859 10860 10861 10862
## [2989] 10863 10864 10865 10866 10867 10868 10869 10870 10871
chrom2.div.small <- chrom2.div[which(chrom2.div$SNP < 8980),]
chrom2.div.small <- chrom2.div.small[which(chrom2.div.small$SNP > 8600),]
# 39200001 to 51650000
tail(chrom2.div.small)
##      CHROM BIN_START  BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 8974     2  54950001 55000000        888        0.00735755    0.00381462
## 8975     2  55000001 55050000        933        0.00726278    0.00291817
## 8976     2  55050001 55100000        914        0.01519790    0.01316560
## 8977     2  55100001 55150000        953        0.00000000    0.00000000
## 8978     2  55150001 55200000        950        0.02453080    0.01712540
## 8979     2  55200001 55250000       1123        0.04151250    0.03948120
##      WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU      PI_UK
## 8974         0.0276795     0.0190044        0.00474647   0.000776248 0.00628362
## 8975         0.0713227     0.0569079        0.02199250   0.012000000 0.00591970
## 8976         0.0273716     0.0252403        0.02200510   0.021276300 0.00620980
## 8977         0.0000000     0.0000000        0.00000000   0.000000000 0.00641686
## 8978         0.0316370     0.0245065        0.00000000   0.000000000 0.00571444
## 8979         0.0553261     0.0473515        0.00000000   0.000000000 0.00691236
##           PI_US      PI_AU TajimaD_UK TajimaD_US TajimaD_AU  SNP   piUK.piAU
## 8974 0.00569739 0.00600235   0.879169   0.756094   0.883948 8974  0.00028127
## 8975 0.00610279 0.00580834   0.666973   0.503085   0.723701 8975  0.00011136
## 8976 0.00523177 0.00562215   0.725060   0.274965   0.841877 8976  0.00058765
## 8977 0.00635051 0.00638269   0.712378   0.737009   0.851269 8977  0.00003417
## 8978 0.00595467 0.00629788   0.408608   0.485645   0.808132 8978 -0.00058344
## 8979 0.00762392 0.00753313   0.871839   0.848962   0.897227 8979 -0.00062077
##        piUK.piUS
## 8974  0.00058623
## 8975 -0.00018309
## 8976  0.00097803
## 8977  0.00006635
## 8978 -0.00024023
## 8979 -0.00071156
quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1)) # set rows
par(mar=c(0,2,0.5,2)) # set margins for each plot
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.5))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_US, col="#2c81a8", lwd=2)
axis(side=4, ylim=c(0,0.04))
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,2.6))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.6))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.6))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,2.4)) # tajima's D axis
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome2.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 6

chrom6.div <- div[which(div$CHROM==6),]
# runs from 16155 to 16871
# chrom 6 = 716 50kb windows ("SNPs" here)

chrom6.div.small <- chrom6.div[which(chrom6.div$SNP < 16325),]
chrom6.div.small <- chrom6.div.small[which(chrom6.div.small$SNP > 16225),]

head(chrom6.div.small)
##       CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 16226     6   3550001 3600000        570        0.01206770   0.008669440
## 16227     6   3600001 3650000        528        0.03079550   0.023766500
## 16228     6   3650001 3700000        701        0.02597620   0.020965100
## 16229     6   3700001 3750000        805        0.00338468   0.000400509
## 16230     6   3750001 3800000        830        0.02082790   0.017915200
## 16231     6   3800001 3850000        800        0.00589529   0.001372410
##       WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 16226         0.0367649     0.0305788         0.0392762     0.0368076
## 16227         0.0501026     0.0406660         0.0739191     0.0613789
## 16228         0.0711590     0.0593933         0.0454643     0.0368937
## 16229         0.0754966     0.0604820         0.0708642     0.0562380
## 16230         0.0375712     0.0322166         0.0438655     0.0358050
## 16231         0.1001270     0.0672706         0.0743417     0.0579428
##            PI_UK      PI_US      PI_AU TajimaD_UK TajimaD_US TajimaD_AU   SNP
## 16226 0.00366513 0.00384209 0.00372947   0.592114   0.972481   0.785959 16226
## 16227 0.00349155 0.00329177 0.00364804   0.682877   0.782069   0.892366 16227
## 16228 0.00468438 0.00458936 0.00478475   0.859767   0.784092   0.972961 16228
## 16229 0.00535166 0.00537373 0.00535428   0.706416   0.819633   0.873451 16229
## 16230 0.00555363 0.00537467 0.00539254   0.840503   0.753410   0.770190 16230
## 16231 0.00531258 0.00519576 0.00516348   0.695610   0.790285   0.812897 16231
##         piUK.piAU   piUK.piUS
## 16226 -0.00006434 -0.00017696
## 16227 -0.00015649  0.00019978
## 16228 -0.00010037  0.00009502
## 16229 -0.00000262 -0.00002207
## 16230  0.00016109  0.00017896
## 16231  0.00014910  0.00011682
chrom6.div.hifst <- chrom6.div[which(chrom6.div$SNP < 16281),]
chrom6.div.hifst <- chrom6.div.small[which(chrom6.div.small$SNP > 16263),]

# AUUK high fst: window 5350001 to 6300001 on Chrom 6
# "SNP" 16263 to 16281

mean(chrom6.div.hifst$PI_UK) 
## [1] 0.002873224
mean(chrom6.div.hifst$PI_US) 
## [1] 0.002896161
mean(chrom6.div.hifst$PI_AU) 
## [1] 0.00293987
chrom6.div.med <- chrom6.div[which(chrom6.div$SNP < 16450),]
chrom6.div.med <- chrom6.div.med[which(chrom6.div.med$SNP > 16155),]
quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1, lty="dashed")
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_US, col="#2c81a8", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,2.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome6.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2
quartz(height=3,width=7)
options(scipen=999)
par(new=T)
## Warning in par(new = T): calling par(new=TRUE) with no plot
plot((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.01,0.4))
lines((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.01,0.4))
lines((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.01,0.4))
lines((chrom6.div.med$BIN_START), chrom6.div.med$WEIGHTED_FST_USAU, col="grey50", lwd=1, lty="dashed")
axis(side=2,ylim=c(-0.01,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome6.BroaderFSTaroundPeak.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 1

chrom1.div <- div[which(div$CHROM==1),]

tail(chrom1.div)
##      CHROM BIN_START   BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 6650     1 113650001 113700000        950        0.02400120    0.01652530
## 6651     1 113700001 113750000        822        0.01785690    0.01123280
## 6652     1 113750001 113800000        752        0.01473210    0.00931469
## 6653     1 113800001 113850000        548        0.00635611    0.00469340
## 6654     1 113850001 113900000         70        0.00000000    0.00000000
## 6655     1 113900001 113950000         18        0.01889640    0.00793244
##      WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 6650        0.00480543    0.00136788        0.03210960    0.02483310
## 6651        0.01020890    0.00436888        0.02774520    0.01840930
## 6652        0.01780170    0.01395660        0.03922310    0.03065050
## 6653        0.03289610    0.02312540        0.03269000    0.02600010
## 6654        0.01299440    0.00724308        0.04291090    0.02511790
## 6655        0.04687790    0.04530420        0.00444444    0.00467634
##            PI_UK       PI_US       PI_AU TajimaD_UK TajimaD_US TajimaD_AU  SNP
## 6650 0.006385910 0.006300220 0.006548810   0.726083  0.8940380   0.928215 6650
## 6651 0.005592130 0.005415310 0.005302000   0.683674  0.7446420   0.698070 6651
## 6652 0.005013190 0.004897800 0.004873960   0.692038  0.7238920   0.758210 6652
## 6653 0.003780760 0.003760340 0.003898600   0.975718  0.9427610   1.127210 6653
## 6654 0.000526667 0.000465180 0.000558005   1.051560  0.4896830   1.272840 6654
## 6655 0.000126333 0.000107167 0.000102833   0.923837 -0.0486854   0.261518 6655
##         piUK.piAU   piUK.piUS
## 6650 -0.000162900 0.000085690
## 6651  0.000290130 0.000176820
## 6652  0.000139230 0.000115390
## 6653 -0.000117840 0.000020420
## 6654 -0.000031338 0.000061487
## 6655  0.000023500 0.000019166
chrom1.div.small <- chrom1.div[which(chrom1.div$SNP < 6720),]
chrom1.div.small <- chrom1.div.small[which(chrom1.div.small$SNP > 6400),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome1.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 1A

chrom1A.div <- div[which(div$CHROM==1.25),]
# 2896 to 4342
# 1446 windows, 3 ticks

chrom1A.div.small <- chrom1A.div[which(chrom1A.div$SNP < 4000),]
chrom1A.div.small <- chrom1A.div.small[which(chrom1A.div.small$SNP > 3400),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome1A.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 4

chrom4.div <- div[which(div$CHROM==4),]
# 13506 to 14923,
# 1417 windows, 7 ticks - peak ~500 windows from start 

chrom4.div.small <- chrom4.div[which(chrom4.div$SNP < 14200),]
chrom4.div.small <- chrom4.div.small[which(chrom4.div.small$SNP > 13800),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_UK, col="#39C855", lwd=1)

axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome4.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2

Chromosome 4A

chrom4A.div <- div[which(div$CHROM==4.5),]
# 13097 to 13505
# 408 windows, 4 ticks

chrom4A.div.small <- chrom4A.div[which(chrom4A.div$SNP < 13350),]
chrom4A.div.small <- chrom4A.div.small[which(chrom4A.div.small$SNP > 13100),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome4A.ManhattanZoom.pdf", type="pdf")
## quartz_off_screen 
##                 2